MEG can map short and long-term changes in brain activity following deep brain stimulation for chronic pain

Deep brain stimulation (DBS) has been shown to be clinically effective for some forms of treatment-resistant chronic pain, but the precise mechanisms of action are not well understood. Here, we present an analysis of magnetoencephalography (MEG) data from a patient with whole-body chronic pain, in order to investigate changes in neural activity induced by DBS for pain relief over both short- and long-term. This patient is one of the few cases treated using DBS of the anterior cingulate cortex (ACC). We demonstrate that a novel method, null-beamforming, can be used to localise accurately brain activity despite the artefacts caused by the presence of DBS electrodes and stimulus pulses. The accuracy of our source localisation was verified by correlating the predicted DBS electrode positions with their actual positions. Using this beamforming method, we examined changes in whole-brain activity comparing pain relief achieved with deep brain stimulation (DBS ON) and compared with pain experienced with no stimulation (DBS OFF). We found significant changes in activity in pain-related regions including the pre-supplementary motor area, brainstem (periaqueductal gray) and dissociable parts of caudal and rostral ACC. In particular, when the patient reported experiencing pain, there was increased activity in different regions of ACC compared to when he experienced pain relief. We were also able to demonstrate long-term functional brain changes as a result of continuous DBS over one year, leading to specific changes in the activity in dissociable regions of caudal and rostral ACC. These results broaden our understanding of the underlying mechanisms of DBS in the human brain.     

Functional Mapping with Simultaneous MEG and EEG

We use magnetoencephalography (MEG) and electroencephalography (EEG) to locate and determine the temporal evolution in brain areas involved in the processing of simple sensory stimuli. We will use somatosensory stimuli to locate the hand somatosensory areas, auditory stimuli to locate the auditory cortices, visual stimuli in four quadrants of the visual field to locate the early visual areas. These type of experiments are used for functional mapping in epileptic and brain tumor patients to locate eloquent cortices. In basic neuroscience similar experimental protocols are used to study the orchestration of cortical activity. The acquisition protocol includes quality assurance procedures, subject preparation for the combined MEG/EEG study, and acquisition of evoked-response data with somatosensory, auditory, and visual stimuli. We also demonstrate analysis of the data using the equivalent current dipole model and cortically-constrained minimum-norm estimates. Anatomical MRI data are employed in the analysis for visualization and for deriving boundaries of tissue boundaries for forward modeling and cortical location and orientation constraints for the minimum-norm estimates.Download video file.^{(69M, mp4)}     

Gustatory evoked cortical activity in humans studied by simultaneous EEG and MEG recording

Evoked potentials are widely used in clinical medicine for objective evaluation of sensory disturbances. However, gustatory evoked potentials (GEPs) have not been extensively studied due to lack of agreement among investigators regarding the waveforms. In this study GEPs and gustatory magnetic fields (GEMfs) were simultaneously recorded from five subjects in response to 0.3 M NaCl in an attempt to establish GEP recording as an objective gustatometer. Each subject received a total of 240 stimulus presentations over six sessions. Three GEP components (P1, N1 and P2) were observed and correlated with their corresponding equivalent current dipoles (ECD1, ECD2 and ECD3, respectively). ECD1 was localized to area G in all subjects, P1 being the indicator of intact gustatory projection to area G. No significant GEP activity was detected during the time preceding P1, which suggests that there was no activity in cortical gyri other than that detected by magnetoencephalography. ECD2 and ECD3 were localized to various cortical structures, including the inferior insula and the superior temporal sulcus, indicating that N1 and P2 reflect higher order gustatory functions. The present results indicate that measurement of GEPs may be useful for objective evaluation of gustatory disturbance.     

Combined EEG/MEG Can Outperform Single Modality EEG or MEG Source Reconstruction in Presurgical Epilepsy Diagnosis

We investigated two important means for improving source reconstruction in presurgical epilepsy diagnosis. The first investigation is about the optimal choice of the number of epileptic spikes in averaging to (1) sufficiently reduce the noise bias for an accurate determination of the center of gravity of the epileptic activity and (2) still get an estimation of the extent of the irritative zone. The second study focuses on the differences in single modality EEG (80-electrodes) or MEG (275-gradiometers) and especially on the benefits of combined EEG/MEG (EMEG) source analysis. Both investigations were validated with simultaneous stereo-EEG (sEEG) (167-contacts) and low-density EEG (ldEEG) (21-electrodes). To account for the different sensitivity profiles of EEG and MEG, we constructed a six-compartment finite element head model with anisotropic white matter conductivity, and calibrated the skull conductivity via somatosensory evoked responses. Our results show that, unlike single modality EEG or MEG, combined EMEG uses the complementary information of both modalities and thereby allows accurate source reconstructions also at early instants in time (epileptic spike onset), i.e., time points with low SNR, which are not yet subject to propagation and thus supposed to be closer to the origin of the epileptic activity. EMEG is furthermore able to reveal the propagation pathway at later time points in agreement with sEEG, while EEG or MEG alone reconstructed only parts of it. Subaveraging provides important and accurate information about both the center of gravity and the extent of the epileptogenic tissue that neither single nor grand-averaged spike localizations can supply.     

Classification methods for ongoing EEG and MEG signals

Classification algorithms help predict the qualitative properties of a subject's mental state by extracting useful information from the highly multivariate non-invasive recordings of his brain activity. In particular, applying them to Magneto-encephalography (MEG) and electro-encephalography (EEG) is a challenging and promising task with prominent practical applications to e.g. Brain Computer Interface (BCI). In this paper, we first review the principles of the major classification techniques and discuss their application to MEG and EEG data classification. Next, we investigate the behavior of classification methods using real data recorded during a MEG visuomotor experiment. In particular, we study the influence of the classification algorithm, of the quantitative functional variables used in this classifier, and of the validation method. In addition, our findings suggest that by investigating the distribution of classifier coefficients, it is possible to infer knowledge and construct functional interpretations of the underlying neural mechanisms of the performed tasks. Finally, the promising results reported here (up to 97% classification accuracy on 1-second time windows) reflect the considerable potential of MEG for the continuous classification of mental states.     

A review of EEG and MEG for brainnetome research

The majority of brain activities are performed by functionally integrating separate regions of the brain. Therefore, the synchronous operation of the brain’s multiple regions or neuronal assemblies can be represented as a network with nodes that are interconnected by links. Because of the complexity of brain interactions and their varying effects at different levels of complexity, one of the corresponding authors of this paper recently proposed the brainnetome as a new –ome to explore and integrate the brain network at different scales. Because electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive and have outstanding temporal resolution and because they are the primary clinical techniques used to capture the dynamics of neuronal connections, they lend themselves to the analysis of the neural networks comprising the brainnetome. Because of EEG/MEG’s applicability to brainnetome analyses, the aim of this review is to identify the procedures that can be used to form a network using EEG/MEG data in sensor or source space and to promote EEG/MEG network analysis for either neuroscience or clinical applications. To accomplish this aim, we show the relationship of the brainnetome to brain networks at the macroscale and provide a systematic review of network construction using EEG and MEG. Some potential applications of the EEG/MEG brainnetome are to use newly developed methods to associate the properties of a brainnetome with indices of cognition or disease conditions. Associations based on EEG/MEG brainnetome analysis may improve the comprehension of the functioning of the brain in neuroscience research or the recognition of abnormal patterns in neurological disease.     

Combined mapping of human auditory EEG and MEG responses

Auditory electric and magnetic P50(m), N1(m) and MMN(m) responses to standard, deviant and novel sounds were studied by recording brain electrical activity with 25 EEG electrodes simultaneously with the corresponding magnetic signals measured with 122 MEG gradiometer coils. The sources of these responses were located on the basis of the MEG responses; all were found to be in the supratemporal plane. The goal of the present paper was to investigate to what degree the source locations and orientations determined from the magnetic data account for the measured EEG signals. It was found that the electric P50, N1 and MMN responses can to a considerable degree be explained by the sources of the corresponding magnetic responses. In addition, source-current components not detectable by MEG were shown to contribute to the measured EEG signals.     

Source Reconstruction Accuracy of MEG and EEG Bayesian Inversion Approaches

Electro- and magnetoencephalography allow for non-invasive investigation of human brain activation and corresponding networks with high temporal resolution. Still, no correct network detection is possible without reliable source localization. In this paper, we examine four different source localization schemes under a common Variational Bayesian framework. A Bayesian approach to the Minimum Norm Model (MNM), an Empirical Bayesian Beamformer (EBB) and two iterative Bayesian schemes (Automatic Relevance Determination (ARD) and Greedy Search (GS)) are quantitatively compared. While EBB and MNM each use a single empirical prior, ARD and GS employ a library of anatomical priors that define possible source configurations. The localization performance was investigated as a function of (i) the number of sources (one vs. two vs. three), (ii) the signal to noise ratio (SNR; 5 levels) and (iii) the temporal correlation of source time courses (for the cases of two or three sources). We also tested whether the use of additional bilateral priors specifying source covariance for ARD and GS algorithms improved performance. Our results show that MNM proves effective only with single source configurations. EBB shows a spatial accuracy of few millimeters with high SNRs and low correlation between sources. In contrast, ARD and GS are more robust to noise and less affected by temporal correlations between sources. However, the spatial accuracy of ARD and GS is generally limited to the order of one centimeter. We found that the use of correlated covariance priors made no difference to ARD/GS performance.     

Modelling and detecting tumour oxygenation levels

Tumours that are low in oxygen (hypoxic) tend to be more aggressive and respond less well to treatment. Knowing the spatial distribution of oxygen within a tumour could therefore play an important role in treatment planning, enabling treatment to be targeted in such a way that higher doses of radiation are given to the more radioresistant tissue. Mapping the spatial distribution of oxygen in vivo is difficult. Radioactive tracers that are sensitive to different levels of oxygen are under development and in the early stages of clinical use. The concentration of these tracer chemicals can be detected via positron emission tomography resulting in a time dependent concentration profile known as a tissue activity curve (TAC). Pharmaco-kinetic models have then been used to deduce oxygen concentration from TACs. Some such models have included the fact that the spatial distribution of oxygen is often highly inhomogeneous and some have not. We show that the oxygen distribution has little impact on the form of a TAC; it is only the mean oxygen concentration that matters. This has significant consequences both in terms of the computational power needed, and in the amount of information that can be deduced from TACs.     

EEG characteristics of bioelectrical activity of the brain in women with normal and complicated pregnancy

Spectrum and coherent EEG analysis has been made at 109 pregnant women during 35-36 weeks of gestation. It has been shown, that functional interhemispherical asymmetry of the brain is the sensible parameter of normal and complicated gestation. It has been found out that as for normal pregnancy prevailing activation in central and temple areas of the brain contralateral to the side of placenta placement in uterus is characteristic. The development of gestation complications accompanied by the inversion of interhemispherical asymmetry of EEG activation. It was pointed out that correlative relations of bioelectrical activity of the brain in women with normal gestation are the indicator of dominating of nervous centers of left hemisphere in the integrational process.     

EEG markers of the disturbed systemic brain activity in hypoxia

Specific rearrangements of the brain bioelectric potential field and the structures where the components (waves) of the main EEG rhythms interact, as well as the stereotactic location and power of the equivalent electrical dipole sources (EEDSs), were studied at various stages of acute experimental hypoxia (breathing for 15–30 min a hypoxic gas mixture containing 8% oxygen in nitrogen). The disrupted intercentral relationships that ensure the formation of the dynamic “morphological equivalent” to support the integrative brain activity, rearrangements of this activity, and the adaptive functions of the whole brain proved to account for partial or complete disintegration of systemic brain activity during acute hypoxia. EEDS tomography showed that EEDSs responsible for the generation of the basic brain rhythmic pattern are normally located in the thalamic structures. At the initial stages of hypoxia, the distribution of the EEDS foci is changed so that the density of EEDSs is increased on the sections that include the hypothalamic region structures, basal nuclei of the forebrain, and the limbic system; the basal, frontal, and medial regions of the temporal lobes of both hemispheres are also involved. With increasing hypoxia, EEDSs appeared in the basal and medial regions of the frontal lobes. At this time, both the surface and deep regions of the frontal lobes of the brain hemispheres are the major targets of the hypoxic effect. At the stages of severe hypoxia, pronounced functional changes in the CNS are observed, including the phenomenon of movement of multiple EEDS foci primarily through the basal and mediobasal regions of the frontal and temporal lobes and in the limbic system structures. Thus, despite the generalized high-amplitude paroxysmal activity that is observed in EEG, a functional disintegration (disruption) of interactions between individual brain regions appears and leads to disturbed regulation of the brain and systemic brain activity. Spatiotemporal EEG markers have been identified that make it possible to assess the individual sensitivity and resistance to hypoxia, as well as the degree of disintegration of his systemic brain activity at different stages of hypoxia.     

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.     

Coupled oscillators for modeling and analysis of EEG/MEG oscillations.

This study presents three EEG/MEG applications in which the modeling of oscillatory signal components offers complementary analysis and an improved explanation of the underlying generator structures. Coupled oscillator networks were used for modeling. Parameters of the corresponding ordinary coupled differential equation (ODE) system are identified using EEG/MEG data and the resulting solution yields the modeled signals. This model-related analysis strategy provides information about the coupling quantity and quality between signal components (example 1, neonatal EEG during quiet sleep), allows identification of the possible contribution of hidden generator structures (example 2, 600-Hz MEG oscillations in somatosensory evoked magnetic fields), and can explain complex signal characteristics such as amplitude-frequency coupling and frequency entrainment (example 3, EEG burst patterns in sedated patients).     

Research on the relation of EEG signal chaos characteristics with high-level intelligence activity of human brain

Using phase space reconstruct technique from one-dimensional and multi-dimensional time series and the quantitative criterion rule of system chaos, and combining the neural network; analyses, computations and sort are conducted on electroencephalogram (EEG) signals of five kinds of human consciousness activities (relaxation, mental arithmetic of multiplication, mental composition of a letter, visualizing a 3-dimensional object being revolved about an axis, and visualizing numbers being written or erased on a blackboard). Through comparative studies on the determinacy, the phase graph, the power spectra, the approximate entropy, the correlation dimension and the Lyapunov exponent of EEG signals of 5 kinds of consciousness activities, the following conclusions are shown: (1) The statistic results of the deterministic computation indicate that chaos characteristic may lie in human consciousness activities, and central tendency measure (CTM) is consistent with phase graph, so it can be used as a division way of EEG attractor. (2) The analyses of power spectra show that ideology of single subject is almost identical but the frequency channels of different consciousness activities have slight difference. (3) The approximate entropy between different subjects exist discrepancy. Under the same conditions, the larger the approximate entropy of subject is, the better the subject's innovation is. (4) The results of the correlation dimension and the Lyapunov exponent indicate that activities of human brain exist in attractors with fractional dimensions. (5) Nonlinear quantitative criterion rule, which unites the neural network, can classify different kinds of consciousness activities well. In this paper, the results of classification indicate that the consciousness activity of arithmetic has better differentiation degree than that of abstract.     

Effect of NMDA-receptor ligands on neocortical and hippocampal EEG activity of rat brain.

Neocortex and hippocampus play important role in motor activity, neuronal plasticity and learning and memory mechanisms. Electroencephalographic (EEG) activity of neocortex and hippocampus of rat following NMDA-receptor agonist, N-methyl-D-aspartate (NMDA), 0.25-2 nmol in 10 microliters, ICV and noncompetitive NMDA-receptor antagonists, MK 801 (0.025-0.1 mg/kg, ip) and ketamine (10-50 mg/kg, ip) at OH, 1/2H, 4H, 8H and 24H was recorded. The electrodes were implanted stereotaxically in hippocampus and neocortex respectively. NMDA (0.25 and 1 nmol) showed longer lasting decrease in amplitude in hippocampus and in frequency in cortical neurons while 2 nmol produced epileptogenic neurotoxicity. Opposite effect i.e. increase in amplitude in both, hippocampus and neocortex was observed with MK 801 and ketamine and these agents also showed longer lasting influence. Administration of MK 801 (0.05 mg/kg) and ketamine (50 mg/kg) prior to NMDA 2 nmol protected 40% animals from NMDA-induced neurotoxicity and blockade of NMDA-induced long term influence. The EEG effect of NMDA agonist and NMDA-induced neurotoxicity at higher dose and its modification by NMDA-antagonist, MK 801 and ketamine suggest that beside NMDA agonists (NMDA), its antagonists may, also affect long lasting changes in hippocampus and cortex. These antagonists reverse NMDA-mediated long term influence in these brain areas.