Where left becomes right: a magnetoencephalographic study of sensorimotor transformation for antisaccades

To perform a saccadic response to a visual stimulus, a 'sensorimotor transformation' is required (i.e., transforming stimulus location into a motor command). Where in the brain is this accomplished? While previous monkey neurophysiology and human fMRI studies examined either parietal cortex or frontal eye field, we studied both of these regions simultaneously using magnetoencephalography (MEG). Nineteen healthy participants performed a pseudorandom series of prosaccades and antisaccades during MEG. Antisaccades require a saccade in the direction opposite a suddenly appearing stimulus. We exploited this dissociation between stimulus and saccadic direction to identify cortical regions that show early activity for a contralateral stimulus and late activity for a contralateral saccade. We found that in the left hemisphere both the intraparietal sulcus and the frontal eye field showed a pattern of activity consistent with sensorimotor transformation - a transition from activity reflecting the direction of the stimulus to that representing the saccadic goal. These findings suggest that sensorimotor transformation is the product of coordinated activity across the intraparietal sulcus and frontal eye field, key components of a cortical network for saccadic generation.     

Saccadic spike potentials in gamma-band EEG: Characterization,detection and suppression

Analysis of high-frequency (gamma-band) neural activity by means of non-invasive EEG is gaining increasing interest. However, we have recently shown that a saccade-related spike potential (SP) seriously confounds the analysis of EEG induced gamma-band responses (iGBR), as the SP eludes traditional EEG artifact rejection methods. Here we provide a comprehensive profile of the SP and evaluate methods for its detection and suppression, aiming to unveil true cerebral gamma-band activity. The SP appears consistently as a sharp biphasic deflection of about 22 ms starting at the saccade onset, with a frequency band of ～ 20–90 Hz. On the average, larger saccades elicit higher SP amplitudes. The SP amplitude gradually changes from the extra-ocular channels towards posterior sites with the steepest gradients around the eyes, indicating its ocular source. Although the amplitude and the sign of the SP depend on the choice of reference channel, the potential gradients remain the same and non-zero for all references. The scalp topography is modulated almost exclusively by the direction of saccades, with steeper gradients ipsilateral to the saccade target. We discuss how the above characteristics impede attempts to remove these SPs from the EEG by common temporal filtering, choice of different references, or rejection of contaminated trials. We examine the extent to which SPs can be reliably detected without an eye tracker, assess the degree to which scalp current density derivation attenuates the effect of the SP, and propose a tailored ICA procedure for minimizing the effect of the SP.     

Source analysis of interictal spikes in polymicrogyria: loss of relevant cortical fissures requires simultaneous EEG to avoid MEG misinterpretation

PURPOSE: Multiple source analysis of interictal EEG and MEG spikes was used to identify irritative zones in polymicrogyria (PMG). Spike onset times and source localization were compared between both modalities. PMG is characterized by a marked loss of deep cortical fissures. Hence, differences between EEG and MEG were expected since MEG signals are predominantly generated from tangentially orientated neurons in fissures. PATIENTS: We studied 7 children and young adults (age 7.5 to 19 years) with localization-related epilepsy and unilateral polymicrogyria (PMG) as defined from anatomical MRI. METHODS: 122-channel whole-head MEG and 32-channel EEG were recorded simultaneously for 25 to 40 min. Using the BESA program, interictal spikes were identified visually and used as templates to search for similar spatio-temporal spike patterns throughout the recording. Detected similar spikes (r > 0.85) were averaged, high-pass filtered (5 Hz) to enhance spike onset, and subjected to multiple spatio-temporal source analysis. Source localization was visualized by superposition on T1-weighted MRI and compared to the lesion. RESULTS: Nine spike types were identified in seven patients (2 types in 2 patients). Eight out of nine EEG sources and seven MEG sources modeling spike onset were localized within the visible lesion. EEG spike onset preceded MEG significantly in two spike types by 19 and 25 ms. This was related to radial onset activity in EEG while MEG localized propagated activity. In one case, the earliest MEG spike activity was localized to the normal hemisphere while the preceding radial EEG onset activity was localized within the lesion. Distances between EEG and MEG onset sources varied markedly between 9 and 51 mm in the eight spike types with concordant lateralization. CONCLUSION: Interictal irritative zones were localized within the lesion in PMG comparable to other malformations, e.g., FCD. Discrepancies in MEG and EEG were related to the lack of deep fissures in PMG. In two cases, MEG was blind to the onset of radial interictal spike activity and localized propagated spike activity. In two other cases, MEG localized to the more peripheral parts of the irritative zone. Simultaneous EEG recordings with MEG and multiple source analysis are required to avoid problems of MEG interpretation.     

Localization of abnormal discharges causing insular epilepsy by magnetoencephalography

The insula, one of the five cerebral lobes of the brain, is located deep within the brain and lies mainly beneath the temporal lobe. Insular epilepsy can be easily confused and misdiagnosed as temporal lobe epilepsy (TLE) because of the similar clinical symptoms and scalp electroencephalography (EEG) findings due to the insula location and neuronal connections with the temporal lobe. Magnetoencephalography (MEG) has higher sensitivity and spatial resolution than scalp EEG, and thus can often identify epileptic discharges not revealed by scalp EEG. Simultaneous scalp EEG and MEG were performed to detect and localize epileptic discharges in two patients known to have insular epilepsy associated with cavernous angioma in the insula. Epileptic discharges were detected as abnormal spikes in the EEG and MEG findings. In Patient 1, the sources of all MEG spikes detected simultaneously by EEG and MEG (E/M-spikes) were localized in the anterior temporal lobe, similar to TLE. In contrast, the sources of all MEG spikes detected only by MEG (M-spikes) were adjacent to the insular lesion. In Patient 2, the sources of all MEG spikes detected simultaneously by EEG and MEG (E/M-spikes) were localized in the anterior temporal lobe. These findings indicate that MEG allows us to detect insular activity that is undetectable by scalp EEG. In conclusion, simultaneous EEG and MEG are helpful for detecting spikes and obtaining additional information about the epileptic origin and propagation in patients with insular epilepsy.     

Grouping of MEG gamma oscillations by EEG sleep spindles

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

Combined MEG/EEG analysis of the interictal spike complex in mesial temporal lobe epilepsy

We studied the functional organization of the interictal spike complex in 30 patients with mesial temporal lobe epilepsy (MTLE) using combined magnetoencephalography (MEG)/electroencephalography (EEG) recordings. Spikes could be recorded in 14 patients (47%) during the 2- to 3-h MEG/EEG recording session. The MEG and EEG spikes were subjected to separate dipole analyses; the MEG spike dipole localizations were superimposed on MRI scans. All spike dipoles could be localized to the temporal lobe with a clear preponderance in the medial region. Based on dipole orientations in MEG, patients could be classified into two groups: patients with anterior medial vertical (AMV) dipoles, suggesting epileptic activity in the mediobasal temporal lobe and patients with anterior medial horizontal (AMH) dipoles, indicating involvement of the temporal pole and the anterior parts of the lateral temporal lobe. Whereas patients with AMV dipoles had strictly unitemporal interictal and ictal EEG changes during prolonged video-EEG monitoring, 50% of patients with AMH dipoles showed evidence of bitemporal affection on interictal and ictal EEG. Nine patients underwent epilepsy surgery so far. Whereas all five patients with AMV dipoles became completely seizure-free postoperatively (Class Ia), two out of four patients with AMH dipoles experienced persistent auras (Class Ib). This difference, however, was not statistically significant. We therefore conclude that combined MEG/EEG dipole modeling can identify subcompartments of the temporal lobe involved in epileptic activity and may be helpful to differentiate between subtypes of mesial temporal lobe epilepsy noninvasively.     

Induced gamma band responses in human EEG after the control of miniature saccadic artifacts

Induced gamma band responses (iGBRs) in the human electroencephalogram (EEG) have been ascribed to the activation of cortical object representations. Recently, this claim was challenged and it was stated that iGBRs occurring in the time window between 200 and 350 ms after stimulus onset are, to a great extent, generated by an electromyogenic artifact caused by miniature saccades (MS). In the present paper we focus on the characterization of iGBRs during the activation of cortical object representations, when recordings have been controlled for saccade-related transient potentials. For this we present an algorithm for the correction of saccade-related transient potentials (COSTRAP) which identifies and notably suppresses transient spike potentials (TSPs) that are likely to be linked to MSs. Furthermore, we conducted an EEG study to demonstrate (1) the feasibility of the algorithm, (2) the cortical origin iGBRs and (3) their relation to cortical object representations. Our results revealed that (i) it is possible to isolate TSPs, (ii) the morphology of the cleansed iGBR cannot be explained by an underlying myogenic artifact and (iii) the remaining iGBRs are sensitive to object recognition. Therefore we conclude that, with saccadic artifacts being controlled, high-frequency oscillations in human EEG are reliable electrophysiological correlates of cognitive processes.     

Epilepsy surgery, resection volume and MSI localization in lesional frontal lobe epilepsy

To verify whether interictal noninvasive information detected by magnetoencephalography (MEG) recordings can contribute to localize focal epileptic activity relevant for seizure generation in lesional frontal lobe epilepsy, magnetic source imaging (MSI) localizations of epileptic discharges were compared to the extent of neurosurgical resection and postoperative outcome. Preoperative MEG spike localizations were displayed in postoperative magnetic resonance imaging (MRI) scans to check whether dipole sites were located within the resection cavity. Moreover, MEG localizations were compared with results of prolonged video-EEG monitoring and, in three cases, with invasive EEG recordings. Our results in five cases with lesional frontal lobe epilepsy showed that good surgical outcome could be achieved in those patients where the majority of MEG spike localizations were located within the resected brain volume.     

Magnetoencephalographic yield of interictal spikes in temporal lobe epilepsy. Comparison with scalp EEG recordings

To compare magnetoencephalography (MEG) with scalp electroencephalography (EEG) in the detection of interictal spikes in temporal lobe epilepsy (TLE), we simultaneously recorded MEG and scalp EEG with a whole-scalp neuromagnetometer in 46 TLE patients. We visually searched interictal spikes on MEG and EEG channels and classified them into three types according to their presentation on MEG alone (M-spikes), EEG alone (E-spikes), or concomitantly on both modalities (M/E-spikes). The M-spikes and M/E-spikes were localized with MEG equivalent current dipole modeling. We analyzed the relative contribution of MEG and EEG in the overall yield of spike detection and also compared M-spikes with M/E-spikes in terms of dipole locations and strengths. During the 30- to 40-min MEG recordings, interictal spikes were obtained in 36 (78.3%) of the 46 patients. Among the 36 patients, most spikes were M/E-spikes (68.3%), some were M-spikes (22.1%), and some were E-spikes (9.7%). In comparison with EEG, MEG gave better spike yield in patients with lateral TLE. Sources of M/E- and M-spikes were situated in the same anatomical regions, whereas the average dipole strength was larger for M/E- than M-spikes. In conclusion, some interictal spikes appeared selectively on either MEG or EEG channels in TLE patients although more spikes were simultaneously identified on both modalities. Thus, simultaneous MEG and EEG recordings help to enhance spike detection. Identification of M-spikes would offer important localization of irritative foci, especially in patients with lateral TLE.     

Recovery of correlated neuronal sources from EEG: the good and bad ways of using SOBI

Second-order blind identification (SOBI) is a blind source separation (BSS) algorithm that has been applied to MEG and EEG data collected during a range of sensory, motor, and cognitive tasks. SOBI can decompose mixtures of electric or magnetic signals by utilizing detailed temporal structures present in the continuously recorded signals. Successful decomposition critically depends on the choice of temporal delay parameters used for computing multiple covariance matrices. Here, we present empirical findings from high-density EEG data (128 channels) to show that SOBI's ability to recover correlated neuronal sources critically depends on the appropriate use of these temporal delay parameters. Specifically, we applied SOBI to EEG data collected during correlated activation of the left and right primary somatosensory cortices (SI). We show that separation of signals originating from the left and right SI is better achieved by using a large number and a wide range of temporal delays between a few and several hundred milliseconds when compared to results using various subsets of these delays. The paper also offers non-mathematician/engineer users a gentle introduction to the inner workings of SOBI.     

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.     

Trial-by-trial coupling between EEG and BOLD identifies networks related to alpha and theta EEG power increases during working memory maintenance

PET and fMRI experiments have previously shown that several brain regions in the frontal and parietal lobe are involved in working memory maintenance. MEG and EEG experiments have shown parametric increases with load for oscillatory activity in posterior alpha and frontal theta power. In the current study we investigated whether the areas found with fMRI can be associated with these alpha and theta effects by measuring simultaneous EEG and fMRI during a modified Sternberg task This allowed us to correlate EEG at the single trial level with the fMRI BOLD signal by forming a regressor based on single trial alpha and theta power estimates. We observed a right posterior, parametric alpha power increase, which was functionally related to decreases in BOLD in the primary visual cortex and in the posterior part of the right middle temporal gyrus. We relate this finding to the inhibition of neuronal activity that may interfere with WM maintenance. An observed parametric increase in frontal theta power was correlated to a decrease in BOLD in regions that together form the default mode network. We did not observe correlations between oscillatory EEG phenomena and BOLD in the traditional WM areas. In conclusion, the study shows that simultaneous EEG-fMRI recordings can be successfully used to identify the emergence of functional networks in the brain during the execution of a cognitive task.     

Validation of SOBI components from high-density EEG

Second-order blind identification (SOBI) is a blind source separation (BSS) algorithm that can be used to decompose mixtures of signals into a set of components or putative recovered sources. Previously, SOBI, as well as other BSS algorithms, has been applied to magnetoencephalography (MEG) and electroencephalography (EEG) data. These BSS algorithms have been shown to recover components that appear to be physiologically and neuroanatomically interpretable. While some proponents of these algorithms suggest that fundamental discoveries about the human brain might be made through the application of these techniques, validation of BSS components has not yet received sufficient attention. Here we present two experiments for validating SOBI-recovered components. The first takes advantage of the fact that noise sources associated with individual sensors can be objectively validated independently from the SOBI process. The second utilizes the fact that the time course and location of primary somatosensory (SI) cortex activation by median nerve stimulation have been extensively characterized using converging imaging methods. In this paper, using both known noise sources and highly constrained and well-characterized neuronal sources, we provide validation for SOBI decomposition of high-density EEG data. We show that SOBI is able to (1) recover known noise sources that were either spontaneously occurring or artificially induced; (2) recover neuronal sources activated by median nerve stimulation that were spatially and temporally consistent with estimates obtained from previous EEG, MEG, and fMRI studies; (3) improve the signal-to-noise ratio (SNR) of somatosensory-evoked potentials (SEPs); and (4) reduce the level of subjectivity involved in the source localization process.     

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.     

The advantage of combining MEG and EEG: comparison to fMRI in focally stimulated visual cortex

To exploit the high (millisecond) temporal resolution of magnetoencephalography (MEG) and electroencephalography (EEG) for measuring neuronal dynamics within well-defined brain regions, it is important to quantitatively assess their localizing ability. Previous modeling studies and empirical data suggest that a combination of MEG and EEG signals should yield the most accurate localization, due to their complementary sensitivities. However, these two modalities have rarely been explicitly combined for source estimation in studies of recorded brain activity, and a quantitative empirical assessment of their abilities, combined and separate, is currently lacking. Here we studied early visual responses to focal Gabor patches flashed during subject fixation. MEG and EEG data were collected simultaneously and were compared with the functional MRI (fMRI) localization produced by identical stimuli in the same subjects. This allowed direct evaluation of the localization accuracy of separate and combined MEG/EEG inverse solutions. We found that the localization accuracy of the combined MEG+EEG solution was consistently better than that of either modality alone, using three different source estimation approaches. Further analysis suggests that this improved localization is due to the different properties of the two imaging modalities rather than simply due to increased total channel number. Thus, combining MEG and EEG data is important for high-resolution spatiotemporal studies of the human brain.     

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.     

Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis

Analysis of spontaneous EEG/MEG needs unsupervised learning methods. While independent component analysis (ICA) has been successfully applied on spontaneous fMRI, it seems to be too sensitive to technical artifacts in EEG/MEG. We propose to apply ICA on short-time Fourier transforms of EEG/MEG signals, in order to find more “interesting” sources than with time-domain ICA, and to more meaningfully sort the obtained components. The method is especially useful for finding sources of rhythmic activity. Furthermore, we propose to use a complex mixing matrix to model sources which are spatially extended and have different phases in different EEG/MEG channels. Simulations with artificial data and experiments on resting-state MEG demonstrate the utility of the method.     

MEG/EEG sources of the 170-ms response to faces are co-localized in the fusiform gyrus

The 170-ms electrophysiological processing stage (N170 in EEG, M170 in MEG) is considered an important computational step in face processing. Hence its neuronal sources have been modelled in several studies. The current study aimed to specify the relation of the dipolar sources underlying N170 and M170. Whole head EEG and MEG were measured simultaneously during the presentation of unfamiliar faces. An Independent Component Analysis (ICA) was applied to the data prior to localization. N170 and M170 were then modelled with a pair of dipoles in a four-shell ellipse (EEG)/homogeneous sphere (MEG) arranged symmetrically across midline. The dipole locations were projected onto the individual structural MR brain images. Dipoles were localized in fusiform gyri in ten out of eleven individuals for EEG and in seven out of eleven for MEG. N170 and M170 were co-localized in the fusiform gyrus in six individuals. The ICA shifted some of the single-subject dipoles up from cerebellum to fusiform gyrus mainly due to the removal of cardiac activity. The group mean dipole locations were also found in posterior fusiform gyri, and did not differ significantly between EEG and MEG. The result was replicated in a repeated measurement 3 months later.     

Bayesian multi-modal model comparison: A case study on the generators of the spike and the wave in generalized spike–wave complexes

We present a novel approach to assess the networks involved in the generation of spontaneous pathological brain activity based on multi-modal imaging data. We propose to use probabilistic fMRI-constrained EEG source reconstruction as a complement to EEG-correlated fMRI analysis to disambiguate between networks that co-occur at the fMRI time resolution. The method is based on Bayesian model comparison, where the different models correspond to different combinations of fMRI-activated (or deactivated) cortical clusters. By computing the model evidence (or marginal likelihood) of each and every candidate source space partition, we can infer the most probable set of fMRI regions that has generated a given EEG scalp data window. We illustrate the method using EEG-correlated fMRI data acquired in a patient with ictal generalized spike–wave (GSW) discharges, to examine whether different networks are involved in the generation of the spike and the wave components, respectively. To this effect, we compared a family of 128 EEG source models, based on the combinations of seven regions haemodynamically involved (deactivated) during a prolonged ictal GSW discharge, namely: bilateral precuneus, bilateral medial frontal gyrus, bilateral middle temporal gyrus, and right cuneus. Bayesian model comparison has revealed the most likely model associated with the spike component to consist of a prefrontal region and bilateral temporal–parietal regions and the most likely model associated with the wave component to comprise the same temporal–parietal regions only. The result supports the hypothesis of different neurophysiological mechanisms underlying the generation of the spike versus wave components of GSW discharges.     

Separate time behaviors of the temporal and frontal mismatch negativity sources

It has been proposed that mismatch negativity (MMN) is generated by temporal and frontal lobe sources, the former being associated with change detection and the latter with involuntary switching of attention to sound change. If this switching of attention is triggered by the temporal cortex change-detection mechanism, one would expect that the frontal component of MMN is activated later than the temporal one. This was studied by using 64-channel electroencephalography (EEG) and 122-channel magnetoencephalography (MEG) with realistically shaped head models to determine the source current distribution in different lobes as a function of time. Minimum-norm estimation (MNE) was performed, constraining the solution to the reconstructed cortical sheet. The results support the hypothesis that the frontal MMN generator is activated later than the auditory cortex generator.