Statistical power: Implications for planning MEG studies

Statistical power is key for robust, replicable science. Here, we systematically explored how numbers of trials and subjects affect statistical power in MEG sensor-level data. More specifically, we simulated "experiments" using the MEG resting-state dataset of the Human Connectome Project (HCP). We divided the data in two conditions, injected a dipolar source at a known anatomical location in the "signal condition", but not in the "noise condition", and detected significant differences at sensor level with classical paired t-tests across subjects, using amplitude, squared amplitude, and global field power (GFP) measures. Group-level detectability of these simulated effects varied drastically with anatomical origin. We thus examined in detail which spatial properties of the sources affected detectability, looking specifically at the distance from closest sensor and orientation of the source, and at the variability of these parameters across subjects. In line with previous single-subject studies, we found that the most detectable effects originate from source locations that are closest to the sensors and oriented tangentially with respect to the head surface. In addition, cross-subject variability in orientation also affected group-level detectability, boosting detection in regions where this variability was small and hindering detection in regions where it was large. Incidentally, we observed a considerable covariation of source position, orientation, and their cross-subject variability in individual brain anatomical space, making it difficult to assess the impact of each of these variables independently of one another. We thus also performed simulations where we controlled spatial properties independently of individual anatomy. These additional simulations confirmed the strong impact of distance and orientation and further showed that orientation variability across subjects affects detectability, whereas position variability does not. Importantly, our study indicates that strict unequivocal recommendations as to the ideal number of trials and subjects for any experiment cannot be realistically provided for neurophysiological studies and should be adapted according to the brain regions under study.

Adapting non-invasive human recordings along multiple task-axes shows unfolding of spontaneous and over-trained choice

Choices rely on a transformation of sensory inputs into motor responses. Using invasive single neuron recordings, the evolution of a choice process has been tracked by projecting population neural responses into state spaces. Here, we develop an approach that allows us to recover similar trajectories on a millisecond timescale in non-invasive human recordings. We selectively suppress activity related to three task-axes, relevant and irrelevant sensory inputs and response direction, in magnetoencephalography data acquired during context-dependent choices. Recordings from premotor cortex show a progression from processing sensory input to processing the response. In contrast to previous macaque recordings, information related to choice-irrelevant features is represented more weakly than choice-relevant sensory information. To test whether this mechanistic difference between species is caused by extensive over-training common in non-human primate studies, we trained humans on >20,000 trials of the task. Choice-irrelevant features were still weaker than relevant features in premotor cortex after over-training.

Lag-invariant detection of interactions in spatially-extended systems using linear inverse modeling

Measurements on physical systems result from the systems' activity being converted into sensor measurements by a forward model. In a number of cases, inversion of the forward model is extremely sensitive to perturbations such as sensor noise or numerical errors in the forward model. Regularization is then required, which introduces bias in the reconstruction of the systems' activity. One domain in which this is particularly problematic is the reconstruction of interactions in spatially-extended complex systems such as the human brain. Brain interactions can be reconstructed from non-invasive measurements such as electroencephalography (EEG) or magnetoencephalography (MEG), whose forward models are linear and instantaneous, but have large null-spaces and high condition numbers. This leads to incomplete unmixing of the forward models and hence to spurious interactions. This motivated the development of interaction measures that are exclusively sensitive to lagged, i.e. delayed interactions. The drawback of such measures is that they only detect interactions that have sufficiently large lags and this introduces bias in reconstructed brain networks. We introduce three estimators for linear interactions in spatially-extended systems that are uniformly sensitive to all lags. We derive some basic properties of and relationships between the estimators and evaluate their performance using numerical simulations from a simple benchmark model.

Non-Invasive Functional-Brain-Imaging with an OPM-based Magnetoencephalography System

A non-invasive functional-brain-imaging system based on optically-pumped-magnetometers (OPM) is presented. The OPM-based magnetoencephalography (MEG) system features 20 OPM channels conforming to the subject's scalp. We have conducted two MEG experiments on three subjects: assessment of somatosensory evoked magnetic field (SEF) and auditory evoked magnetic field (AEF) using our OPM-based MEG system and a commercial MEG system based on superconducting quantum interference devices (SQUIDs). We cross validated the robustness of our system by calculating the distance between the location of the equivalent current dipole (ECD) yielded by our OPM-based MEG system and the ECD location calculated by the commercial SQUID-based MEG system. We achieved sub-centimeter accuracy for both SEF and AEF responses in all three subjects. Due to the proximity (12 mm) of the OPM channels to the scalp, it is anticipated that future OPM-based MEG systems will offer enhanced spatial resolution as they will capture finer spatial features compared to traditional MEG systems employing SQUIDs.

Permutation Statistics for Connectivity Analysis between Regions of Interest in EEG and MEG Data

Connectivity estimates based on electroencephalography (EEG) and magnetoencephalography (MEG) are unique in their ability to provide neurophysiologically meaningful spectral and temporal information non-invasively. This multi-dimensional aspect of the MEG/EEG based connectivity increases the challenges of the analysis and interpretation of the data. Many MEG/EEG studies address this complexity by using a hypothesis-driven approach, which focuses on particular regions of interest (ROI). However, if an effect is distributed unevenly over a large ROI and variable across subjects, it may not be detectable using conventional methods. Here, we propose a novel approach, which enhances the statistical power for weak and spatially discontinuous effects. This results in the ability to identify statistically significant connectivity patterns with spectral, temporal, and spatial specificity while correcting for multiple comparisons using nonparametric permutation methods. We call this new approach the Permutation Statistics for Connectivity Analysis between ROI (PeSCAR). We demonstrate the processing steps with simulated and real human data. The open-source Matlab code implementing PeSCAR are provided online.

Spatial neuronal synchronization and the waveform of oscillations: Implications for EEG and MEG

Neuronal oscillations are ubiquitous in the human brain and are implicated in virtually all brain functions. Although they can be described by a prominent peak in the power spectrum, their waveform is not necessarily sinusoidal and shows rather complex morphology. Both frequency and temporal descriptions of such non-sinusoidal neuronal oscillations can be utilized. However, in non-invasive EEG/MEG recordings the waveform of oscillations often takes a sinusoidal shape which in turn leads to a rather oversimplified view on oscillatory processes. In this study, we show in simulations how spatial synchronization can mask non-sinusoidal features of the underlying rhythmic neuronal processes. Consequently, the degree of non-sinusoidality can serve as a measure of spatial synchronization. To confirm this empirically, we show that a mixture of EEG components is indeed associated with more sinusoidal oscillations compared to the waveform of oscillations in each constituent component. Using simulations, we also show that the spatial mixing of the non-sinusoidal neuronal signals strongly affects the amplitude ratio of the spectral harmonics constituting the waveform. Finally, our simulations show how spatial mixing can affect the strength and even the direction of the amplitude coupling between constituent neuronal harmonics at different frequencies. Validating these simulations, we also demonstrate these effects in real EEG recordings. Our findings have far reaching implications for the neurophysiological interpretation of spectral profiles, cross-frequency interactions, as well as for the unequivocal determination of oscillatory phase.

Modeling neuronal avalanches and long-range temporal correlations at the emergence of collective oscillations: Continuously varying exponents mimic M/EEG results

We revisit the CROS ("CRitical OScillations") model which was recently proposed as an attempt to reproduce both scale-invariant neuronal avalanches and long-range temporal correlations. With excitatory and inhibitory stochastic neurons locally connected in a two-dimensional disordered network, the model exhibits a transition where alpha-band oscillations emerge. Precisely at the transition, the fluctuations of the network activity have nontrivial detrended fluctuation analysis (DFA) exponents, and avalanches (defined as supra-threshold activity) have power law distributions of size and duration. We show that, differently from previous results, the exponents governing the distributions of avalanche size and duration are not necessarily those of the mean-field directed percolation universality class (3/2 and 2, respectively). Instead, in a narrow region of parameter space, avalanche exponents obtained via a maximum-likelihood estimator vary continuously and follow a linear relation, in good agreement with results obtained from M/EEG data. In that region, moreover, the values of avalanche and DFA exponents display a spread with positive correlations, reproducing human MEG results.

Deep brain activities can be detected with magnetoencephalography

The hippocampus and amygdala are key brain structures of the medial temporal lobe, involved in cognitive and emotional processes as well as pathological states such as epilepsy. Despite their importance, it is still unclear whether their  neural activity can be recorded non-invasively. Here, using simultaneous intracerebral and magnetoencephalography (MEG) recordings in patients with focal drug-resistant epilepsy, we demonstrate a direct contribution of amygdala and hippocampal activity to surface MEG recordings. In particular, a method of blind source separation, independent component analysis, enabled activity arising from large neocortical networks to be disentangled from that of deeper structures, whose amplitude at the surface was small but significant. This finding is highly relevant for our understanding of hippocampal and amygdala brain activity as it implies that their activity could potentially be measured non-invasively.

Real-Time Clustered Multiple Signal Classification (RTC-MUSIC)

Magnetoencephalography (MEG) and electroencephalography provide a high temporal resolution, which allows estimation of the detailed time courses of neuronal activity. However, in real-time analysis of these data two major challenges must be handled: the low signal-to-noise ratio (SNR) and the limited time available for computations. In this work, we present real-time clustered multiple signal classification (RTC-MUSIC) a real-time source localization algorithm, which can handle low SNRs and can reduce the computational effort. It provides correlation information together with sparse source estimation results, which can, e.g., be used to identify evoked responses with high sensitivity. RTC-MUSIC clusters the forward solution based on an anatomical brain atlas and optimizes the scanning process inherent to MUSIC approaches. We evaluated RTC-MUSIC by analyzing MEG auditory and somatosensory data. The results demonstrate that the proposed method localizes sources reliably. For the auditory experiment the most dominant correlated source pair was located bilaterally in the superior temporal gyri. The highest activation in the somatosensory experiment was found in the contra-lateral primary somatosensory cortex.

Similarities and differences between on-scalp and conventional in-helmet magnetoencephalography recordings

The development of new magnetic sensor technologies that promise sensitivities approaching that of conventional MEG technology while operating at far lower operating temperatures has catalysed the growing field of on-scalp MEG. The feasibility of on-scalp MEG has been demonstrated via benchmarking of new sensor technologies performing neuromagnetic recordings in close proximity to the head surface against state-of-the-art in-helmet MEG sensor technology. However, earlier work has provided little information about how these two approaches compare, or about the reliability of observed differences. Herein, we present such a comparison, based on recordings of the N20m component of the somatosensory evoked field as elicited by electric median nerve stimulation. As expected from the proximity differences between the on-scalp and in-helmet sensors, the magnitude of the N20m activation as recorded with the on-scalp sensor was higher than that of the in-helmet sensors. The dipole pattern of the on-scalp recordings was also more spatially confined than that of the conventional recordings. Our results furthermore revealed unexpected temporal differences in the peak of the N20m component. An analysis protocol was therefore developed for assessing the reliability of this observed difference. We used this protocol to examine our findings in terms of differences in sensor sensitivity between the two types of MEG recordings. The measurements and subsequent analysis raised attention to the fact that great care has to be taken in measuring the field close to the zero-line crossing of the dipolar field, since it is heavily dependent on the orientation of sensors. Taken together, our findings provide reliable evidence that on-scalp and in-helmet sensors measure neural sources in mostly similar ways.

Accounting for linear transformations of EEG and MEG data in source analysis

Analyses of electro- and magnetoencephalography (EEG, MEG) data often involve a linear modification of signals at the sensor level. Examples include re-referencing of the EEG, computation of synthetic gradiometer in MEG, or the removal of artifactual independent components to clean EEG and MEG data. A question of practical relevance is, if such modifications must be accounted for by adapting the physical forward model (leadfield) before subsequent source analysis. Here, we show that two scenarios need to be differentiated. In the first scenario, which corresponds to re-referencing the EEG and synthetic gradiometer computation in MEG, the leadfield must be adapted before source analysis. In the second scenario, which corresponds to removing artifactual components to 'clean' the data, the leadfield must not be changed. We demonstrate and discuss the consequences of wrongly modifying the leadfield in the latter case for an example. Future EEG and MEG studies employing source analyses should carefully consider whether and, if so, how the leadfield must be modified as explicated here.

Reconstructing coherent networks from electroencephalography and magnetoencephalography with reduced contamination from volume conduction or magnetic field spread

Volume conduction (VC) and magnetic field spread (MFS) induce spurious correlations between EEG/MEG sensors, such that the estimation of functional networks from scalp recordings is inaccurate. Imaginary coherency [1] reduces VC/MFS artefacts between sensors by assuming that instantaneous interactions are caused predominantly by VC/MFS and do not contribute to the imaginary part of the cross-spectral densities (CSDs). We propose an adaptation of the dynamic imaging of coherent sources (DICS) [2] - a method for reconstructing the CSDs between sources, and subsequently inferring functional connectivity based on coherences between those sources. Firstly, we reformulate the principle of imaginary coherency by performing an eigenvector decomposition of the imaginary part of the CSD to estimate the power that only contributes to the non-zero phase-lagged (NZPL) interactions. Secondly, we construct an NZPL-optimised spatial filter with two a priori assumptions: (1) that only NZPL interactions exist at the source level and (2) the NZPL CSD at the sensor level is a good approximation of the projected source NZPL CSDs. We compare the performance of the NZPL method to the standard method by reconstructing a coherent network from simulated EEG/MEG recordings. We demonstrate that, as long as there are phase differences between the sources, the NZPL method reliably detects the underlying networks from EEG and MEG. We show that the method is also robust to very small phase lags, noise from phase jitter, and is less sensitive to regularisation parameters. The method is applied to a human dataset to infer parts of a coherent network underpinning face recognition.

Cross-Approximate Entropy parallel computation on GPUs for biomedical signal analysis. Application to MEG recordings

Cross-Approximate Entropy (Cross-ApEn) is a useful measure to quantify the statistical dissimilarity of two time series. In spite of the advantage of Cross-ApEn over its one-dimensional counterpart (Approximate Entropy), only a few studies have applied it to biomedical signals, mainly due to its high computational cost. In this paper, we propose a fast GPU-based implementation of the Cross-ApEn that makes feasible its use over a large amount of multidimensional data. The scheme followed is fully scalable, thus maximizes the use of the GPU despite of the number of neural signals being processed. The approach consists in processing many trials or epochs simultaneously, with independence of its origin. In the case of MEG data, these trials can proceed from different input channels or subjects. The proposed implementation achieves an average speedup greater than 250× against a CPU parallel version running on a processor containing six cores. A dataset of 30 subjects containing 148 MEG channels (49 epochs of 1024 samples per channel) can be analyzed using our development in about 30min. The same processing takes 5 days on six cores and 15 days when running on a single core. The speedup is much larger if compared to a basic sequential Matlab(®) implementation, that would need 58 days per subject. To our knowledge, this is the first contribution of Cross-ApEn measure computation using GPUs. This study demonstrates that this hardware is, to the day, the best option for the signal processing of biomedical data with Cross-ApEn.

Population level inference for multivariate MEG analysis

Multivariate analysis is a very general and powerful technique for analysing Magnetoencephalography (MEG) data. An outstanding problem however is how to make inferences that are consistent over a group of subjects as to whether there are condition-specific differences in data features, and what are those features that maximise these differences. Here we propose a solution based on Canonical Variates Analysis (CVA) model scoring at the subject level and random effects Bayesian model selection at the group level. We apply this approach to beamformer reconstructed MEG data in source space. CVA estimates those multivariate patterns of activation that correlate most highly with the experimental design; the order of a CVA model is then determined by the number of significant canonical vectors. Random effects Bayesian model comparison then provides machinery for inferring the optimal order over the group of subjects. Absence of a multivariate dependence is indicated by the null model being the most likely. This approach can also be applied to CVA models with a fixed number of canonical vectors but supplied with different feature sets. We illustrate the method by identifying feature sets based on variable-dimension MEG power spectra in the primary visual cortex and fusiform gyrus that are maximally discriminative of data epochs before versus after visual stimulation.

A beamformer analysis of MEG data reveals frontal generators of the musically elicited mismatch negativity

To localize the neural generators of the musically elicited mismatch negativity with high temporal resolution we conducted a beamformer analysis (Synthetic Aperture Magnetometry, SAM) on magnetoencephalography (MEG) data from a previous musical mismatch study. The stimuli consisted of a six-tone melodic sequence comprising broken chords in C- and G-major. The musical sequence was presented within an oddball paradigm in which the last tone was lowered occasionally (20%) by a minor third. The beamforming analysis revealed significant right hemispheric neural activation in the superior temporal (STC), inferior frontal (IFC), superior frontal (SFC) and orbitofrontal (OFC) cortices within a time window of 100-200 ms after the occurrence of a deviant tone. IFC and SFC activation was also observed in the left hemisphere. The pronounced early right inferior frontal activation of the auditory mismatch negativity has not been shown in MEG studies so far. The activation in STC and IFC is consistent with earlier electroencephalography (EEG), optical imaging and functional magnetic resonance imaging (fMRI) studies that reveal the auditory and inferior frontal cortices as main generators of the auditory MMN. The observed right hemispheric IFC is also in line with some previous music studies showing similar activation patterns after harmonic syntactic violations. The results demonstrate that a deviant tone within a musical sequence recruits immediately a distributed neural network in frontal and prefrontal areas suggesting that top-down processes are involved when expectation violation occurs within well-known stimuli.

Regularized logistic regression and multiobjective variable selection for classifying MEG data

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori.

Source cancellation profiles of electroencephalography and magnetoencephalography

Recorded electric potentials and magnetic fields due to cortical electrical activity have spatial spread even if their underlying brain sources are focal. Consequently, as a result of source cancellation, loss in signal amplitude and reduction in the effective signal-to-noise ratio can be expected when distributed sources are active simultaneously. Here we investigate the cancellation effects of EEG and MEG through the use of an anatomically correct forward model based on structural MRI acquired from 7 healthy adults. A boundary element model (BEM) with four compartments (brain, cerebrospinal fluid, skull and scalp) and highly accurate cortical meshes (~300,000 vertices) were generated. Distributed source activations were simulated using contiguous patches of active dipoles. To investigate cancellation effects in both EEG and MEG, quantitative indices were defined (source enhancement, cortical orientation disparity) and computed for varying values of the patch radius as well as for automatically parcellated gyri and sulci. Results were calculated for each cortical location, averaged over all subjects using a probabilistic atlas, and quantitatively compared between MEG and EEG. As expected, MEG sensors were found to be maximally sensitive to signals due to sources tangential to the scalp, and minimally sensitive to radial sources. Compared to EEG, however, MEG was found to be much more sensitive to signals generated antero-medially, notably in the anterior cingulate gyrus. Given that sources of activation cancel each other according to the orientation disparity of the cortex, this study provides useful methods and results for quantifying the effect of source orientation disparity upon source cancellation.

A general Bayesian treatment for MEG source reconstruction incorporating lead field uncertainty

There is uncertainty introduced when a cortical surface based model derived from an anatomical MRI is used to reconstruct neural activity with MEG data. This is a specific case of a problem with uncertainty in parameters on which M/EEG lead fields depend non-linearly. Here we present a general mathematical treatment of any such problem with a particular focus on co-registration. We use a Metropolis search followed by Bayesian Model Averaging over multiple sparse prior source inversions with different headlocation/orientation parameters. Based on MEG data alone we can locate the cortex to within 4mm at empirically realistic signal to noise ratios. We also show that this process gives improved posterior distributions on the estimated current distributions, and can be extended to make inference on the locations of local maxima by providing confidence intervals for each source.

Comparative power spectral analysis of simultaneous elecroencephalographic and magnetoencephalographic recordings in humans suggests non-resistive extracellular media

The resistive or non-resistive nature of the extracellular space in the brain is still debated, and is an important issue for correctly modeling extracellular potentials. Here, we first show theoretically that if the medium is resistive, the frequency scaling should be the same for electroencephalogram (EEG) and magnetoencephalogram (MEG) signals at low frequencies (<10 Hz). To test this prediction, we analyzed the spectrum of simultaneous EEG and MEG measurements in four human subjects. The frequency scaling of EEG displays coherent variations across the brain, in general between 1/f and 1/f(2), and tends to be smaller in parietal/temporal regions. In a given region, although the variability of the frequency scaling exponent was higher for MEG compared to EEG, both signals consistently scale with a different exponent. In some cases, the scaling was similar, but only when the signal-to-noise ratio of the MEG was low. Several methods of noise correction for environmental and instrumental noise were tested, and they all increased the difference between EEG and MEG scaling. In conclusion, there is a significant difference in frequency scaling between EEG and MEG, which can be explained if the extracellular medium (including other layers such as dura matter and skull) is globally non-resistive.

Cognitive abilities and 50- and 100-msec paired-click processes in schizophrenia

OBJECTIVE

Abnormal 50- and 100-msec event-related brain activity derived from paired-click procedures are well established in schizophrenia. There is little agreement on whether group differences in the ratio score, i.e., the ratio of EEG amplitude after the second stimulus (S2) to the amplitude after the first stimulus (S1), reflect an encoding or gating abnormality. In addition, the functional implications remain unclear. In the present study, EEG and magnetoencephalography (MEG) were used to examine paired-click measures and cognitive correlates of paired-click activity.

METHOD

EEG and whole-cortex MEG data were acquired during the standard paired-click paradigm in 73 comparison subjects and 79 schizophrenia patients. Paired-click ratio scores were obtained at 50 msec (P50 evoked potential at Cz, M50 at left and right superior temporal gyrus [STG]) and 100 msec (N100 at Cz, M100 at left and right STG). A cognitive battery assessing attention, working memory, and long-delay memory was administered. IQ was also estimated.

RESULTS

Groups differed on ratio score and amplitude of S1 response. Ratio scores at 50 msec and 100 msec and S1 amplitude predicted variance in attention (primarily S1 amplitude), working memory, and long-delay memory. The attention findings remained after removal of variance associated with IQ.

CONCLUSIONS

Associations between paired-click measures and cognitive performance in patients support 50-msec and 100-msec ratio and amplitude scores as clinically significant biomarkers of schizophrenia. In general, cognitive performance was better predicted by the ability to encode auditory information than the ability to filter redundant information.

Magnetoencephalography demonstrates multiple asynchronous generators during human sleep spindles

Sleep spindles are approximately 1 s bursts of 10-16 Hz activity that occur during stage 2 sleep. Spindles are highly synchronous across the cortex and thalamus in animals, and across the scalp in humans, implying correspondingly widespread and synchronized cortical generators. However, prior studies have noted occasional dissociations of the magnetoencephalogram (MEG) from the EEG during spindles, although detailed studies of this phenomenon have been lacking. We systematically compared high-density MEG and EEG recordings during naturally occurring spindles in healthy humans. As expected, EEG was highly coherent across the scalp, with consistent topography across spindles. In contrast, the simultaneously recorded MEG was not synchronous, but varied strongly in amplitude and phase across locations and spindles. Overall, average coherence between pairs of EEG sensors was approximately 0.7, whereas MEG coherence was approximately 0.3 during spindles. Whereas 2 principle components explained approximately 50% of EEG spindle variance, >15 were required for MEG. Each PCA component for MEG typically involved several widely distributed locations, which were relatively coherent with each other. These results show that, in contrast to current models based on animal experiments, multiple asynchronous neural generators are active during normal human sleep spindles and are visible to MEG. It is possible that these multiple sources may overlap sufficiently in different EEG sensors to appear synchronous. Alternatively, EEG recordings may reflect diffusely distributed synchronous generators that are less visible to MEG. An intriguing possibility is that MEG preferentially records from the focal core thalamocortical system during spindles, and EEG from the distributed matrix system.

Evaluation of spectral ratio measures from spontaneous MEG recordings in patients with Alzheimer's disease

Alzheimer's disease (AD) is the most frequent form of dementia in western countries. The rise in life expectancy will likely increase its prevalence, since ageing is the greatest known risk factor. Although an early and accurate identification is critical, low diagnostic accuracy is currently reached. Hence, the aim of the present study was to analyse the spontaneous magnetoencephalographic (MEG) activity from 148 channels in 20 AD patients and 21 healthy controls to extract discriminating spectral features. Relative power (RP) was calculated in conventional frequency bands and several ratios were defined to emphasise the differences in its distribution. Both RP values and spectral ratios were transformed with a principal component analysis to summarise information with minimal loss of variability. AD patients showed a significant increase of RP(delta) and RP(theta), along with a decrease of RP(beta) and RP(gamma). The most significant differences were reached by spectral ratios using the beta band. Specifically, we obtained 75.0% sensitivity, 90.5% specificity and 82.9% accuracy (linear discriminant analysis with a leave-one-out cross-validation procedure), together with a p-value lower than 0.001 (one-way analysis of variance with age as a covariate) using the [RP(alpha)+RP(beta(1))+RP(beta(2))+RP(gamma)]/[RP(delta)+RP(theta)] ratio. The spectral ratios also showed a higher correlation with the severity of dementia than individual relative power measures. Our results suggest that the spectral ratios could be useful descriptors to help in the AD diagnosis, since they effectively summarise the slowing of the AD patients' MEG rhythms in individual indexes and correlate significantly with the severity of dementia.

Neuromagnetic Localization of Spike Sources in Perilesional, Contralateral Mirror, and Ipsilateral Remote Areas in Patients with Cavernoma

PURPOSE

To assess neuromagnetic spike localization as an indication for extended lesionectomy of cavernoma.

METHODS

Electroencephalography (EEG) and magnetoencephalography (MEG) was simultaneously recorded in 17 patients (8 men, mean age 29.7 years) with single cavernoma. The location of the equivalent current dipole (ECD) of the interictal spikes was correlated with the lesion shown by magnetic resonance imaging.

RESULTS

Preoperative ECD localization was classified into four types: perilesional, adjacent to the cavernoma only (n = 6); mirror, adjacent to the lesion and at the contralateral homologous site (n = 5); remote, mainly at a remote site in the ipsilateral hemisphere (n = 3); and no spikes (n = 3). The spikes were detected by only MEG in two of five "mirror" and all three "remote" patients. In the mirror group, contralateral spikes were synchronized with the ipsilateral spikes, or also occurred independently. Two "perilesional" and two "mirror" patients became seizure-free and spike-free after extended lesionectomy. In contrast, the other two "mirror" patients had residual seizures and spikes after pure lesionectomy.

CONCLUSION

The detectability of mirror and remote spikes was higher by MEG than by EEG, whereas the detectability of perilesional spikes was similar by MEG and EEG. Therefore, the use of both EEG and MEG will provide the maximum information about spike distribution and propagation. Residual seizures and spikes after pure lesionectomy, but not after extended lesionectomy, in the "mirror" patients suggest the importance of resection of the perilesional irritable zone. Extended resection of the irritable cortex surrounding cavernoma is recommended for better seizure control, particularly in "mirror" patients.

Utility of Magnetoencephalography in the Evaluation of Recurrent Seizures after Epilepsy Surgery

PURPOSE

To study the role of magnetoencephalography (MEG) in the surgical evaluation of children with recurrent seizures after epilepsy surgery.

METHODS

We studied 17 children with recurrent seizures after epilepsy surgery using interictal and ictal scalp EEG, intracranial video EEG (IVEEG), MRI, and MEG. We analyzed the location and distribution of MEG spike sources (MEGSSs) and the relationship of MEGSSs to the margins of previous resections and surgical outcome.

RESULTS

Clustered MEGSSs occurred at the margins of previous resections within two contiguous gyri in 10 patients (group A), extended spatially from a margin by < or =3 cm in three patients (group B), and were remote from a resection margin by >3 cm in six patients (group C). Two patients had concomitant group A and C clusters. Thirteen patients underwent second surgeries. IVEEG was used in four patients. Six of seven patients with group A MEGSS clusters did not require IVEEG for second surgeries. Follow-up periods ranged from 0.6 to 4.3 years (mean: 2.6 years). Eleven children, including eight who became seizure-free, achieved Engel class I or II.

CONCLUSION

Our data demonstrate the utility of MEG for evaluating patients with recurrent seizures after epilepsy surgery. Specific MEGSS cluster patterns delineate epileptogenic zones. Removing cluster regions adjacent to the margins of previous resections, in addition to removing recurrent lesions, achieves favorable surgical outcome. Cluster location and extent identify which patients require IVEEG, potentially eliminating IVEEG for some. Patients with remotely located clusters require IVEEG for accurate assessment and localization of the entire epileptogenic zone.

Magnetoencephalography Is More Successful for Screening and Localizing Frontal Lobe Epilepsy than Electroencephalography

PURPOSE

The diagnosis of frontal lobe epilepsy may be compounded by poor electroclinical localization, due to distributed or rapidly propagating epileptiform activity. This study aimed at developing optimal procedures for localizing interictal epileptiform discharges (IEDs) of patients with localization related epilepsy in the frontal lobe. To this end the localization results obtained for magnetoencephalography (MEG) and electroencephalography (EEG) were compared systematically using automated analysis procedures.

METHODS

Simultaneous recording of interictal EEG and MEG was successful for 18 out of the 24 patients studied. Visual inspection of these recordings revealed IEDs with varying morphology and topography. Cluster analysis was used to classify these discharges on the basis of their spatial distribution followed by equivalent dipole analysis of the cluster averages. The locations of the equivalent dipoles were compared with the location of the epileptogenic lesions of the patient or, if these were not visible at MRI with the location of the interictal onset zones identified by subdural electroencephalography.

RESULTS

Generally IEDs were more abundantly in MEG than in the EEG recordings. Furthermore, the duration of the MEG spikes, measured from the onset till the spike maximum, was in most patients shorter than the EEG spikes. In most patients, distinct spike subpopulations were found with clearly different topographical field maps. Cluster analysis of MEG spikes followed by dipole localization was successful (n = 14) for twice as many patients as for EEG source analysis (n = 7), indicating that the localizability of interictal MEG is much better than of interictal EEG.

CONCLUSIONS

The automated procedures developed in this study provide a fast screening method for identifying the distinct categories of spikes and the brain areas responsible for these spikes. The results show that MEG spike yield and localization is superior compared with EEG. This finding is of importance for the diagnosis and preoperative evaluation of patients with frontal lobe epilepsy.

Magnetoencephalographic studies of focal epileptic activity in three patients with epilepsy suggestive of Lennox-Gastaut syndrome

PURPOSE

To determine the electromagnetic sources of localized epileptic activities using magnetoencephalography (MEG) in three adult patients with epilepsy suggestive of Lennox-Gastaut syndrome (LGS).

METHODS

MEG and simultaneous electroencephalography (EEG) were recorded from three adult patients using a 204-channel, whole-head MEG system. Equivalent current dipoles (ECDs) were calculated for epileptic spikes on MEG according to the single dipole model.

RESULTS

In two patients, MEG showed epileptiform discharges restricted to the unilateral temporal area, corresponding to the EEG spikes. The ECDs calculated from these MEG spikes were clustered in the unilateral temporal lobe. In our third patient, MEG spikes appeared in the right centroparietal area; ECDs were located to the right parietal lobe.

CONCLUSIONS

The sources of epileptiform discharges that were detected in a restricted area were localized to specific parts of the brain cortex. Despite certain limitations (small number of patients; atypical late-onset epilepsy in one) our study suggests that MEG may prove to be a useful tool for investigating electromagnetic features of localized epileptic discharges in patients with LGS. Based on these preliminary results, further studies performed in patients with typical LGS features are justified.

Hierarchical Bayesian estimates of distributed MEG sources: Theoretical aspects and comparison of variational and MCMC methods

Magnetoencephalography (MEG) provides millisecond-scale temporal resolution for noninvasive mapping of human brain functions, but the problem of reconstructing the underlying source currents from the extracranial data has no unique solution. Several distributed source estimation methods based on different prior assumptions have been suggested for the resolution of this inverse problem. Recently, a hierarchical Bayesian generalization of the traditional minimum norm estimate (MNE) was proposed, in which the variance of distributed current at each cortical location is considered as a random variable and estimated from the data using the variational Bayesian (VB) framework. Here, we introduce an alternative scheme for performing Bayesian inference in the context of this hierarchical model by using Markov chain Monte Carlo (MCMC) strategies. In principle, the MCMC method is capable of numerically representing the true posterior distribution of the currents whereas the VB approach is inherently approximative. We point out some potential problems related to hyperprior selection in the previous work and study some possible solutions. A hyperprior sensitivity analysis is then performed, and the structure of the posterior distribution as revealed by the MCMC method is investigated. We show that the structure of the true posterior is rather complex with multiple modes corresponding to different possible solutions to the source reconstruction problem. We compare the results from the VB algorithm to those obtained from the MCMC simulation under different hyperparameter settings. The difficulties in using a unimodal variational distribution as a proxy for a truly multimodal distribution are also discussed. Simulated MEG data with realistic sensor and source geometries are used in performing the analyses.

Quantitative EEG and Electromagnetic Brain Imaging in Aging and in the Evolution of Dementia

Electroencephalographic (EEG) changes with normal aging have long been reported. Departures from age-expected changes have been observed in mild cognitive impairment and dementia, the magnitude of which correlates with the degree of cognitive impairment. Such abnormalities include increased delta and theta activity, decreased mean frequency, and changes in coherence. Similar findings have been reported using magnetoencephalography (MEG) at rest and during performance of mental tasks. Electrophysiological features have also been shown to be predictive of future decline in mild cognitive impairment (MCI) and Alzheimer's disease (AD). We have recently reported results from initial quantitative electroencephalography (QEEG) evaluations of normal elderly subjects (with only subjective reports of memory loss), predicting future cognitive decline or conversion to dementia, with high prediction accuracy (approximately 95%). In this report, source localization algorithms were used to identify the mathematically most probable underlying generators of abnormal features of the scalp-recorded EEG from these patients with differential outcomes. Using this QEEG method, abnormalities in brain regions identified in studies of AD using MEG, MRI, and positron emission tomography (PET) imaging were found in the premorbid recordings of those subjects who go on to decline or convert to dementia.

Searching for the best model: ambiguity of inverse solutions and application to fetal magnetoencephalography

Fetal brain signals produce weak magnetic fields at the maternal abdominal surface. In the presence of much stronger interference these weak fetal fields are often nearly indistinguishable from noise. Our initial objective was to validate these weak fetal brain fields by demonstrating that they agree with the electromagnetic model of the fetal brain. The fetal brain model is often not known and we have attempted to fit the data to not only the brain source position, orientation and magnitude, but also to the brain model position. Simulation tests of this extended model search on fetal MEG recordings using dipole fit and beamformers revealed a region of ambiguity. The region of ambiguity consists of a family of models which are not distinguishable in the presence of noise, and which exhibit large and comparable SNR when beamformers are used. Unlike the uncertainty of a dipole fit with known model plus noise, this extended ambiguity region yields nearly identical forward solutions, and is only weakly dependent on noise. The ambiguity region is located in a plane defined by the source position, orientation, and the true model centre, and will have a diameter approximately 0.67 of the modelled fetal head diameter. Existence of the ambiguity region allows us to only state that the fetal brain fields do not contradict the electromagnetic model; we can associate them with a family of models belonging to the ambiguity region, but not with any specific model. In addition to providing a level of confidence in the fetal brain signals, the ambiguity region knowledge in combination with beamformers allows detection of undistorted temporal waveforms with improved signal-to-noise ratio, even though the source position cannot be uniquely determined.

Analysis of magnetic source localization of P300 using the multiple signal classification algorithm

The authors studied the localization of P300 magnetic sources using the multiple signal classification (MUSIC) algorithm. Six healthy subjects (aged 24-34 years old) were investigated with 148-channel whole-head type magnetencephalography using an auditory oddball paradigm in passive mode. The authors also compared six stimulus combinations in order to find the optimal stimulus parameters for P300 magnetic field (P300m) in passive mode. Bilateral MUSIC peaks were located on the mesial temporal, superior temporal and parietal lobes. Interestingly, all MUSIC peaks in these regions emerged earlier in the right hemisphere than in the left hemisphere, suggesting that the right hemisphere has predominance over the left in the processing activity associated with P300m. There were no significant differences among the six stimulus combinations in evoking those P300m sources. The results of the present study suggest that the MUSIC algorithm could be a useful tool for analysis of the time-course of P300m.

Neuromagnetic Source Localization of Epileptiform Activity in Patients with Graphogenic Epilepsy

PURPOSE

To clarify the source localization of epileptiform activity by using magnetoencephalography (MEG) in patients with graphogenic epilepsy.

METHODS

MEG and simultaneous EEG were recorded with a 204-channel whole-head MEG system in two patients with graphogenic epilepsy. During the MEG recordings, the patients performed a set of tasks comprising mental arithmetic calculation, speaking, moving the right arm in a manner resembling writing, writing, and thinking of writing. Equivalent current dipoles (ECD) were calculated for epileptiform discharges on MEG by using a single-dipole model. The ECD were superimposed on the magnetic resonance images of the patients.

RESULTS

The task of writing provoked seizures, in which both patients jerked the right arms. Thinking of writing also induced these seizures. In both patients, EEG associated with the seizures showed bursts of spike-and-slow-wave complexes predominantly in the centroparietal region. MEG also showed epileptiform discharges corresponding to the EEG bursts. ECDs obtained from the discharges were clustered in the left centroparietal area.

CONCLUSIONS

Thinking of writing was a trigger for the seizures, as well as the task of writing. The source of the epileptiform discharge associated with the seizures was localized in the unilateral centroparietal area. The findings suggest that the centroparietal region plays an important role in the pathophysiology underlying these two graphogenic epilepsy cases.

A technique to consider mismatches between fMRI and EEG/MEG sources for fMRI-constrained EEG/MEG source imaging: a preliminary simulation study

fMRI-constrained EEG/MEG source imaging can be a powerful tool in studying human brain functions with enhanced spatial and temporal resolutions. Recent studies on the combination of fMRI and EEG/MEG have suggested that fMRI prior information could be readily implemented by simply imposing different weighting factors to cortical sources overlapping with the fMRI activations. It has been also reported, however, that such a hard constraint may cause severe distortions or elimination of meaningful EEG/MEG sources when there are distinct mismatches between the fMRI activations and the EEG/MEG sources. If one wants to obtain the actual EEG/MEG source locations and uses the fMRI prior information as just an auxiliary tool to enhance focality of the distributed EEG/MEG sources, it is reasonable to weaken the strength of fMRI constraint when severe mismatches between fMRI and EEG/MEG sources are observed. The present study suggests an efficient technique to automatically adjust the strength of fMRI constraint according to the mismatch level. The use of the proposed technique rarely affects the results of conventional fMRI-constrained EEG/MEG source imaging if no major mismatch between the two modalities is detected; while the new results become similar to those of typical EEG/MEG source imaging without fMRI constraint if the mismatch level is significant. A preliminary simulation study using realistic EEG signals demonstrated that the proposed technique can be a promising tool to selectively apply fMRI prior information to EEG/MEG source imaging.

FDG-PET/MRI coregistration and diffusion-tensor imaging distinguish epileptogenic tubers and cortex in patients with tuberous sclerosis complex: a preliminary report

PURPOSE

Patients with tuberous sclerosis complex (TSC) are potential surgical candidates if the epileptogenic region(s) can be accurately identified. This retrospective study determined whether FDG-PET/MRI coregistration and diffusion-tensor imaging (DTI) showed better accuracy in the localization of epileptogenic cortex than structural MRI in TSC patients.

METHODS

FDG-PET/MRI coregistration and/or DTI for apparent diffusion coefficient (ADC) and fractional anisotropy (FA) were utilized in 15 TSC patients. Presurgery scalp EEG and postsurgery seizure control identified epileptogenic tubers (n = 27) and these were compared with nonepileptogenic tubers (n = 204) for MRI tuber volume, volume of FDG-PET hypometabolism on MRI coregistration, DTI, ADC, and FA values.

RESULTS

Compared with nonepileptogenic tubers, epileptogenic regions had increased volume of FDG-PET hypometabolism (p < 0.0001), and increased ADC values in subtuber white matter (p < 0.0001). In contrast, the largest MRI identified tuber (p = 0.046) and decreased FA values (p = 0.58) were less accurate in identifying epileptogenic regions. Larger volumes of FDG-PET hypometabolism correlated positively with increased ADC values (p = 0.029), and localized to areas of cortical dysplasia adjacent to the tuber in four cases.

CONCLUSIONS

Larger volumes of FDG-PET hypometabolism relative to MRI tuber size and higher ADC values identified epileptogenic tubers and adjoining cortex containing cortical dysplasia in TSC patients with improved accuracy compared with largest tuber by MRI or lowest FA values. Used in conjunction with ictal scalp EEG and interictal magnetoencephalography, these newer neuroimaging techniques should improve the noninvasive evaluation of TSC patients with intractable epilepsy in distinguishing epileptogenic sites for surgical resection.

Controlled Support MEG imaging

In this paper, we present a novel approach to imaging sparse and focal neural current sources from MEG (magnetoencephalography) data. Using the framework of Tikhonov regularization theory, we introduce a new stabilizer that uses the concept of controlled support to incorporate a priori assumptions about the area occupied by focal sources. The paper discusses the underlying Tikhonov theory and its relationship to a Bayesian formulation which in turn allows us to interpret and better understand other related algorithms.

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.

Magnetoencephalography in Patients with Tuberous Sclerosis and Localization-related Epilepsy

PURPOSE

To clarify the usefulness of magnetoencephalography (MEG) for diagnosis of the spatial relations between spike foci and suspicious epileptogenic tubers on MRI in patients with tuberous sclerosis (TS) and to compare MEG spike foci with single-photon emission computed tomography (SPECT) findings.

METHODS

We analyzed magnetic fields of epileptic spike discharges in 15 patients with TS and localization-related epilepsy (LRE) by using MEG (a whole-head 204-channel magnetometer system). We investigated the spatial relation between the equivalent current dipoles (ECDs) of interictal spike discharges and visible cortical tubers on MRI. We also compared results of MEG and MRI with SPECT findings.

RESULTS

MEG detected a cluster of ECDs around one cortical tuber in six of 15 patients and clusters of ECDs around two cortical tubers in five patients. Interictal SPECT was disappointing in detection of epileptic foci in TS. However, MEG spike foci showed spatial consistency with ictal hyperperfusion areas in two patients. Three patients with single ECD clusters underwent surgical treatment: two have been seizure free, and one has obtained seizure reduction of >90%.

CONCLUSIONS

ECDs were located around visible tuber nodules. MEG enabled precise localization of the epileptic foci and provided crucial information for surgical treatment in patients with TS and partial epilepsy. TS patients showing a single ECD cluster on MEG may be appropriate candidates for surgical treatment.

Disordered semantic representation in schizophrenic temporal cortex revealed by neuromagnetic response patterns

BACKGROUND

Loosening of associations and thought disruption are key features of schizophrenic psychopathology. Alterations in neural networks underlying this basic abnormality have not yet been sufficiently identified. Previously, we demonstrated that spatio-temporal clustering of magnetic brain responses to pictorial stimuli map categorical representations in temporal cortex. This result has opened the possibility to quantify associative strength within and across semantic categories in schizophrenic patients. We hypothesized that in contrast to controls, schizophrenic patients exhibit disordered representations of semantic categories.

METHODS

The spatio-temporal clusters of brain magnetic activities elicited by object pictures related to super-ordinate (flowers, animals, furniture, clothes) and base-level (e.g. tulip, rose, orchid, sunflower) categories were analysed in the source space for the time epochs 170-210 and 210-450 ms following stimulus onset and were compared between 10 schizophrenic patients and 10 control subjects.

RESULTS

Spatio-temporal correlations of responses elicited by base-level concepts and the difference of within vs. across super-ordinate categories were distinctly lower in patients than in controls. Additionally, in contrast to the well-defined categorical representation in control subjects, unsupervised clustering indicated poorly defined representation of semantic categories in patients. Within the patient group, distinctiveness of categorical representation in the temporal cortex was positively related to negative symptoms and tended to be inversely related to positive symptoms.

CONCLUSION

Schizophrenic patients show a less organized representation of semantic categories in clusters of magnetic brain responses than healthy adults. This atypical neural network architecture may be a correlate of loosening of associations, promoting positive symptoms.

Localizing complex neural circuits with MEG data

During cognitive processing, the various cortical areas, with specialized functions, supply for different tasks. In most cases then, the information flows are processed in a parallel way by brain networks which work together integrating the single performances for a common goal. Such a step is generally performed at higher processing levels in the associative areas. The frequency range at which neuronal pools oscillate is generally wider than the one which is detectable by bold changes in fMRI studies. A high time resolution technique like magnetoencephalography or electroencephalography is therefore required as well as new data processing algorithms for detecting different coherent brain areas cooperating for one cognitive task. Our experiments show that no algorithm for the inverse problem solution is immune from bias. We propose therefore, as a possible solution, our software LOCANTO (LOcalization and Coherence ANalysis TOol). This new package features a set of tools for the detection of coherent areas. For such a task, as a default, it employs the algorithm with best performances for the neural landscape to be detected. If the neural landscape under attention involves more than two interacting areas the SLoreta algorithm is used. Our study shows in fact that SLoreta performance is not biased when the correlation among multiple sources is high. On the other hand, the Beamforming algorithm is more precise than SLoreta at localizing single or double sources but it gets a relevant localization bias when the sources are more than three and are highly correlated.

MEG source localization under multiple constraints: An extended Bayesian framework

To use Electroencephalography (EEG) and Magnetoencephalography (MEG) as functional brain 3D imaging techniques, identifiable distributed source models are required. The reconstruction of EEG/MEG sources rests on inverting these models and is ill-posed because the solution does not depend continuously on the data and there is no unique solution in the absence of prior information or constraints. We have described a general framework that can account for several priors in a common inverse solution. An empirical Bayesian framework based on hierarchical linear models was proposed for the analysis of functional neuroimaging data [Friston, K., Penny, W., Phillips, C., Kiebel, S., Hinton, G., Ashburner, J., 2002. Classical and Bayesian inference in neuroimaging: theory. NeuroImage 16, 465-483] and was evaluated recently in the context of EEG [Phillips, C., Mattout, J., Rugg, M.D., Maquet, P., Friston, K., 2005. An empirical Bayesian solution to the source reconstruction problem in EEG. NeuroImage 24, 997-1011]. The approach consists of estimating the expected source distribution and its conditional variance that is constrained by an empirically determined mixture of prior variance components. Estimation uses Expectation-Maximization (EM) to give the Restricted Maximum Likelihood (ReML) estimate of the variance components (in terms of hyperparameters) and the Maximum A Posteriori (MAP) estimate of the source parameters. In this paper, we extend the framework to compare different combinations of priors, using a second level of inference based on Bayesian model selection. Using Monte-Carlo simulations, ReML is first compared to a classic Weighted Minimum Norm (WMN) solution under a single constraint. Then, the ReML estimates are evaluated using various combinations of priors. Both standard criterion and ROC-based measures were used to assess localization and detection performance. The empirical Bayes approach proved useful as: (1) ReML was significantly better than WMN for single priors; (2) valid location priors improved ReML source localization; (3) invalid location priors did not significantly impair performance. Finally, we show how model selection, using the log-evidence, can be used to select the best combination of priors. This enables a global strategy for multiple prior-based regularization of the MEG/EEG source reconstruction.

Single and Multiple Clusters of Magnetoencephalographic Dipoles in Neocortical Epilepsy: Significance in Characterizing the Epileptogenic Zone

PURPOSE

To characterize the epileptogenic zone in neocortical epilepsy (NE) by using magnetoencephalography (MEG).

METHODS

We defined and compared locations of single and multiple clusters of equivalent current dipoles (ECDs) for interictal spikes with MRI findings, ictal-onset zones (IOZs) from subdural electroencephalography (SDEEG), resected areas, and postsurgical outcomes of 20 patients who underwent cortical resection for medically intractable NE.

RESULTS

Fourteen patients had single clusters; six had multiple clusters. Overlap of clusters and IOZs defined group A (nine patients), in which a single cluster coincided with the IOZ; group B1 (four patients), in which a single cluster was within or partially overlapped the IOZ; group B2 (five patients), in which multiple-cluster sections overlapped IOZs; group C (two patients; one single; one multiple), in which no overlap was seen. More single clusters (nine of 14) than multiple clusters (none of six) coincided with the IOZ (p = 0.014). More patients with single clusters (10 of 14) than patients with multiple clusters (one of six) had seizure-free outcomes (p = 0.049). Eight of nine patients in group A, versus three of 11 in groups B1, B2, and C, achieved seizure-free outcomes (p = 0.0098). Correlations between MRI findings and postsurgical outcomes were not statistically significant; eight of 13 patients with single lesions, one of four with no lesions, and two of three with multifocal lesions had seizure-free outcomes.

CONCLUSIONS

In neocortical epilepsy, MEG ECD clusters correlated with SDEEG IOZs. Single clusters indicated discrete epileptogenic zones that required complete resection for seizure-free outcome. Multiple clusters necessitated that the multiple or extensive epileptogenic zones be completely identified and delineated by SDEEG.

Data-driven parceling and entropic inference in MEG

In Amblard et al. [Amblard, C., Lapalme, E., Lina, J.M. 2004. Biomagnetic source detection by maximum entropy and graphical models. IEEE Trans. Biomed. Eng. 55 (3) 427--442], the authors introduced the maximum entropy on the mean (MEM) as a methodological framework for solving the magnetoencephalography (MEG) inverse problem. The main component of the MEM is a reference probability density that enables one to include all kind of prior information on the source intensity distribution to be estimated. This reference law also encompasses the definition of a model. We consider a distributed source model together with a clustering hypothesis that assumes functionally coherent dipoles. The reference probability distribution is defined as a prior parceling of the cortical surface. In this paper, we present a data-driven approach for parceling out the cortex into regions that are functionally coherent. Based on the recently developed multivariate source pre-localization (MSP) principle [Mattout, J., Pelegrini-Issac, M., Garnero, L., Benali, H. 2005. Multivariate source pre-localization (MSP): Use of functionally informed basis functions for better conditioning the MEG inverse problem. NeuroImage 26 (2) 356--373], the data-driven clustering (DDC) of the dipoles provides an efficient parceling of the sources as well as an estimate of parameters of the initial reference probability distribution. On MEG simulated data, the DDC is shown to further improve the MEM inverse approach, as evaluated considering two different iterative algorithms and using classical error metrics as well as ROC (receiver operating characteristic) curve analysis. The MEM solution is also compared to a LORETA-like inverse approach. The data-driven clustering allows to take most advantage of the MEM formalism. Its main trumps lie in the flexible probabilistic way of introducing priors and in the notion of spatial coherent regions of activation. The latter reduces the dimensionality of the problem. In so doing, it narrows down the gap between the two types of inverse methods, the popular dipolar approaches and the distributed ones.

Frequency flows and the time-frequency dynamics of multivariate phase synchronization in brain signals

The quantification of phase synchrony between brain signals is of crucial importance for the study of large-scale interactions in the brain. Current methods are based on the estimation of the stability of the phase difference between pairs of signals over a time window, within successive frequency bands. This paper introduces a new approach to study the dynamics of brain synchronies, Frequency Flows Analysis (FFA). It allows direct tracking and characterization of the nonstationary time-frequency dynamics of phase synchrony among groups of signals. It is based on the use of the one-to-one relationship between frequency locking and phase synchrony, which applies when the concept of phase synchrony is not taken in an extended 'statistical' sense of a bias in the distribution of phase differences, but in the sense of a continuous phase difference conservation during a short period of time. In such a case, phase synchrony implies identical instantaneous frequencies among synchronized signals, with possible time varying frequencies of synchronization. In this framework, synchronous groups of signals or neural assemblies can be identified as belonging to common frequency flows, and the problem of studying synchronization becomes the problem of tracking frequency flows. We use the ridges of the analytic wavelet transforms of the signals of interest in order to estimate maps of instantaneous frequencies and reveal sustained periods of common instantaneous frequency among groups of signal. FFA is shown to track complex dynamics of synchrony in coupled oscillator models, reveal the time-frequency and spatial dynamics of synchrony convergence and divergence in epileptic seizures, and in MEG data the large-scale ongoing dynamics of synchrony correlated with conscious perception during binocular rivalry.

Identification of the Epileptogenic Tuber in Patients with Tuberous Sclerosis: A Comparison of High-resolution EEG and MEG

PURPOSE

We compared epileptiform activity recorded with EEG and magnetoencephalography (MEG) in 19 patients with tuberous sclerosis complex (TSC) and epilepsy.

METHODS

High-resolution (HR) EEG, HR-MEG, and 1.5-T MRI scans were performed. Epileptiform spikes were identified in EEG and MEG recordings offline by three observers. Spikes for which the interobserver agreement (spike consensus) was >0.40 were used for source localization with CURRYV 3.0 software. MUSIC analysis was performed. The distance between the source determined from EEG and MEG recordings and the border of the closest tuber was calculated and compared.

RESULTS

Consensus spikes (kappa >0.4) were identified in 12 patients in the EEG recording and in 14 patients in the MEG recording. MEG sources were closer to tubers in all but one patient. Three patients underwent epilepsy surgery, two of whom are seizure free after complete resection of the tuber.

CONCLUSIONS

In patients with TSC, epileptogenic sources identified on MEG are closer to the presumed epileptogenic tuber than are similar sources identified on EEG. Moreover, spike consensus is greater with MEG. Clear identification of the epileptogenic zone may offer opportunities for surgery in patients with TSC with intractable epilepsy.

A graphical model for estimating stimulus-evoked brain responses from magnetoencephalography data with large background brain activity

This paper formulates a novel probabilistic graphical model for noisy stimulus-evoked MEG and EEG sensor data obtained in the presence of large background brain activity. The model describes the observed data in terms of unobserved evoked and background factors with additive sensor noise. We present an expectation maximization (EM) algorithm that estimates the model parameters from data. Using the model, the algorithm cleans the stimulus-evoked data by removing interference from background factors and noise artifacts and separates those data into contributions from independent factors. We demonstrate on real and simulated data that the algorithm outperforms benchmark methods for denoising and separation. We also show that the algorithm improves the performance of localization with beamforming algorithms.

Linear inverse solutions: simulations from a realistic head model in MEG

Distributed linear solutions are widely used in source localization to solve the ill-posed EEG/MEG inverse problem. In the classical approach based on dipole sources, these methods estimate the current densities at a great number of brain sites, typically at the nodes of a 3-D grid which discretizes the chosen solution space. The estimated current density distributions are displayed as brain electromagnetic tomography (BET) images. We have tested well known minimum norm solutions (MN, WMN, LORETA) and other linear inverse solutions [WROP, sLORETA, interference uniform, gain uniform, weight vector normalized (WVN), and a new solution named SLF (Standardized Lead Field)], using a MEG configuration (BTi Magnes 2500 WH with 148 axial magnetometers) and a realistic head model using BEM (Boundary Element Method). The solutions were compared in a noise-free condition and in the presence of noise using the classical dipole localization errors (DLE) together with a new figure of merit that we called max gain uniformity, which measures the capability of an inverse linear solution to show spots of activity with similar amplitudes on the brain electromagnetic tomographies when multiple dipole sources with similar moments are simultaneously active. Whereas some solutions (sLORETA, interference uniform and SLF) were capable of zero dipole localization errors in the noise-free case, none of them reached 100% of correct dipole localizations in the presence of a high level of Gaussian noise. The SLF solution, which has the advantage to be independent from any regularization parameter, presented the best results with the lowest max gain uniformities, with almost 100% of correct dipole localizations with 10% of noise and more than 90% of correct localizations with 30% of noise added to the data. Nevertheless, no solution was able to combine at the same time a correct localization of single sources and the capability to visualize multiple sources with comparable amplitudes on the brain electromagnetic tomographies.

Integrated MEG and fMRI model: synthesis and analysis

An integrated model for magnetoencephalography (MEG) and functional Magnetic Resonance Imaging (fMRI) is proposed. In the model, the neural activity is related to the Post Synaptic Potentials (PSPs) which is common link between MEG and fMRI. Each PSP is modeled by the direction and strength of its current flow which are treated as random variables. The overall neural activity in each voxel is used for equivalent current dipole in MEG and as input of extended Balloon model in fMRI. The proposed model shows the possibility of detecting activation by fMRI in a voxel while the voxel is silent for MEG and vice versa. Parameters of the model can illustrate situations like closed field due to non-pyramidal cells, canceling effect of inhibitory PSP on excitatory PSP, and effect of synchronicity. In addition, the model shows that the crosstalk from neural activities of the adjacent voxels in fMRI may result in the detection of activations in these voxels that contain no neural activities. The proposed model is instrumental in evaluating and comparing different analysis methods of MEG and fMRI. It is also useful in characterizing the upcoming combined methods for simultaneous analysis of MEG and fMRI.

Timing of early activity in the visual cortex as revealed by simultaneous MEG and ERG recordings

To clarify the latency of the earliest cortical activity in visual processing, electroretinograms (ERGs) and visual evoked magnetic fields (VEFs) following flash stimulation were recorded simultaneously in six human subjects. Flash stimuli were applied to the right eye and ERGs were recorded from a skin electrode placed on the lower lid. ERGs showed two major deflections in all subjects: an eyelid-negativity around 20 ms and a positivity around 60 ms corresponding to an a- and b-waves, respectively. The mean onset and peak latency of the earliest component of VEFs (37 M) was 30.2 and 36.9 ms, respectively. There was a linear correlation between the peak latency of the a-wave and the onset latency of the 37 M (r=0.90, P=0.011). When a single equivalent current dipole analysis was applied to the 37 M, four out of six subjects showed highly reliable results. The generator of the 37 M was estimated to be located in the striate cortex in all four subjects. Since post-receptoral activities in the retina are expected to start around the peak of the a-wave (20 ms), the early cortical activity, which appears 10 ms later than the a-wave peak, is considered to be the earliest cortical activity following flash stimulation.

Nonlinear dynamical analysis of EEG and MEG: review of an emerging field

Many complex and interesting phenomena in nature are due to nonlinear phenomena. The theory of nonlinear dynamical systems, also called 'chaos theory', has now progressed to a stage, where it becomes possible to study self-organization and pattern formation in the complex neuronal networks of the brain. One approach to nonlinear time series analysis consists of reconstructing, from time series of EEG or MEG, an attractor of the underlying dynamical system, and characterizing it in terms of its dimension (an estimate of the degrees of freedom of the system), or its Lyapunov exponents and entropy (reflecting unpredictability of the dynamics due to the sensitive dependence on initial conditions). More recently developed nonlinear measures characterize other features of local brain dynamics (forecasting, time asymmetry, determinism) or the nonlinear synchronization between recordings from different brain regions. Nonlinear time series has been applied to EEG and MEG of healthy subjects during no-task resting states, perceptual processing, performance of cognitive tasks and different sleep stages. Many pathologic states have been examined as well, ranging from toxic states, seizures, and psychiatric disorders to Alzheimer's, Parkinson's and Cre1utzfeldt-Jakob's disease. Interpretation of these results in terms of 'functional sources' and 'functional networks' allows the identification of three basic patterns of brain dynamics: (i) normal, ongoing dynamics during a no-task, resting state in healthy subjects; this state is characterized by a high dimensional complexity and a relatively low and fluctuating level of synchronization of the neuronal networks; (ii) hypersynchronous, highly nonlinear dynamics of epileptic seizures; (iii) dynamics of degenerative encephalopathies with an abnormally low level of between area synchronization. Only intermediate levels of rapidly fluctuating synchronization, possibly due to critical dynamics near a phase transition, are associated with normal information processing, whereas both hyper-as well as hyposynchronous states result in impaired information processing and disturbed consciousness.

Persistent idiopathic facial pain exists independent of somatosensory input from the painful region: findings from quantitative sensory functions and somatotopy of the primary somatosensory cortex

In 14 patients with unilateral persistent idiopathic facial pain (PIFP), classified according to the criteria of the International Headache Society, and 16 age-matched control subjects sensory functions were examined on the face by quantitative sensory testing (QST). Additionally, the somatotopy of the primary somatosensory cortex (SI) to tactile input from the pain area was evaluated by means of magnetoencephalography. Previously reported abnormalities in PIFP as a dishabituation of the R2 component of the blink reflex and psychiatric disturbances were co-evaluated. Psychiatric evaluation included a Structured Clinical Interview for axis-I DSM IV disorders (SCID-I) and employment of the SCL-90-R and a depression scale (ADS). Thresholds to touch, pin prick, warm, cold, heat and pressure pain as well as the pain ratings to single and repetitive (perceptual wind up) painful pin prick stimuli did not indicate a significant sensory deficit or hyperactivity in the pain area when compared with the asymptomatic side nor when compared with the values of healthy control subjects. QST results were not significantly altered in patients (n=4) that showed an abnormal dishabituation of the R2 component of the blink reflex. The interhemispheric difference in distance between the cortical representation of the lip and the index finger did not differ between patients and control subjects. Psychiatric evaluation did not disclose significant abnormalities at a group level. It is concluded that PIFP is maintained by mechanisms which do not involve somatosensory processing of stimuli from the pain area.