Time-coherent expansion of MEG/EEG cortical sources

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

Localization of realistic cortical activity in MEG using current multipoles

We present a novel approach to MEG source estimation based on a regularized first-order multipole solution. The Gaussian regularizing prior is obtained by calculation of the sample mean and covariance matrix for the equivalent moments of realistic simulated cortical activity. We compare the regularized multipole localization framework to the classical dipole and general multipole source estimation methods by evaluating the ability of all three solutions to localize the centroids of physiologically plausible patches of activity simulated on the surface of a human cerebral cortex. The results, obtained with a realistic sensor configuration, a spherical head model, and given in terms of field and localization error, depict the performance of the dipolar and multipolar models as a function of variable source surface area (50-500 mm(2)), noise conditions (20, 10, and 5 dB SNR), source orientation (0-90 degrees ), and source depth (3-11 cm). We show that as the sources increase in size, they become less accurately modeled as current dipoles. The regularized multipole systematically outperforms the single dipole model, increasingly so as the spatial extent of the sources increases. In addition, our simulations demonstrate that as the orientation of the sources becomes more radial, dipole localization accuracy decreases substantially, while the performance of the regularized multipole model is far less sensitive to orientation and even succeeds in localizing quasi-radial source configurations. Furthermore, our results show that the multipole model is able to localize superficial sources with higher accuracy than the current dipole. These results indicate that the regularized multipole solution may be an attractive alternative to current-dipole-based source estimation methods in MEG.     

Bayesian model averaging in EEG/MEG imaging

In this paper, the Bayesian Theory is used to formulate the Inverse Problem (IP) of the EEG/MEG. This formulation offers a comparison framework for the wide range of inverse methods available and allows us to address the problem of model uncertainty that arises when dealing with different solutions for a single data. In this case, each model is defined by the set of assumptions of the inverse method used, as well as by the functional dependence between the data and the Primary Current Density (PCD) inside the brain. The key point is that the Bayesian Theory not only provides for posterior estimates of the parameters of interest (the PCD) for a given model, but also gives the possibility of finding posterior expected utilities unconditional on the models assumed. In the present work, this is achieved by considering a third level of inference that has been systematically omitted by previous Bayesian formulations of the IP. This level is known as Bayesian model averaging (BMA). The new approach is illustrated in the case of considering different anatomical constraints for solving the IP of the EEG in the frequency domain. This methodology allows us to address two of the main problems that affect linear inverse solutions (LIS): (a) the existence of ghost sources and (b) the tendency to underestimate deep activity. Both simulated and real experimental data are used to demonstrate the capabilities of the BMA approach, and some of the results are compared with the solutions obtained using the popular low-resolution electromagnetic tomography (LORETA) and its anatomically constraint version (cLORETA).     

A general linear model for MEG beamformer imaging

A new general linear model (GLM) beamformer method is described for processing magnetoencephalography (MEG) data. A standard nonlinear beamformer is used to determine the time course of neuronal activation for each point in a predefined source space. A Hilbert transform gives the envelope of oscillatory activity at each location in any chosen frequency band (not necessary in the case of sustained (DC) fields), enabling the general linear model to be applied and a volumetric T statistic image to be determined. The new method is illustrated by a two-source simulation (sustained field and 20 Hz) and is shown to provide accurate localization. The method is also shown to locate accurately the increasing and decreasing gamma activities to the temporal and frontal lobes, respectively, in the case of a scintillating scotoma. The new method brings the advantages of the general linear model to the analysis of MEG data and should prove useful for the localization of changing patterns of activity across all frequency ranges including DC (sustained fields).     

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.     

Localization of sensorimotor cortical rhythms induced by tactile stimulation using spatially filtered MEG

We applied the synthetic aperture magnetometry (SAM) spatial filtering method to localize sensorimotor mu (8-14 Hz) and beta (15-35 Hz) rhythms following tactile (brush) stimulation. Neuromagnetic activity was recorded from 10 adult subjects. Transient brush stimuli were applied separately to the right index finger, medial right toe and lower right lip. Differential images of mu and beta band source power were created for periods during (event-related desynchronization; ERD) or following (event-related synchronization; ERS) tactile stimulation, relative to prestimulus baseline activity. Mu ERD to finger brushing was localized to the contralateral somatosensory cortex and was organized somatotopically. Mu ERS, however, was not consistently observed for each subject. Beta ERD was consistently localized to sensory cortical areas and organized somatotopically in the post-central gyrus (SI), and beta ERS was observed to be organized motorotopically in the precentral gyrus (MI). Longer duration (2-3 s) stimulation of the index finger also produced beta ERS in the primary motor cortex, and its time course demonstrated that these oscillatory changes are an off-response to the termination of the presented sensory stimulus. Interestingly, lip and toe stimulation also produced post-stimulus increases in beta rhythms in the bilateral motor hand areas for all subjects, suggesting that common neural systems in the primary motor cortex are activated during tactile stimulation of different body regions.     

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

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

MEG imaging of sensorimotor areas using inter-trial coherence in vibrotactile steady-state responses

We utilized a novel analysis technique to identify brain areas that activate synchronously during the steady-state interval of responses to vibrotactile stimulation of the right index finger. The inter-trial coherence at the stimulation rate (23 Hz) was determined for whole-brain neural activity estimates based on a linearly-constrained minimum variance beamformer applied to the MEG data. Neural activity coherent with the stimulus occurred in the contralateral primary somatosensory cortex in all subjects, and matched well with equivalent dipole modeling of the same data. Subsets of subjects exhibited additional loci of strongly coherent activity in the contralateral primary motor cortex, posterior parietal cortex, and supplementary motor area, as well as in deeper brain structures above the brainstem. An activation delay of 7 ms from deep structures to cortical areas was estimated based on the mean phase at each coherent neural source within a single subject. This new approach - volumetric mapping of the statistical parameter of inter-trial coherence in steady-state oscillations - broadens the range of MEG beamformer applications specifically for identifying brain areas that are synchronized to repetitive stimuli.     

Finding parametric representations of the cortical sulci using an active contour model

The cortical sulci are brain structures resembling thin convoluted ribbons embedded in three dimensions. The importance of the sulci lies primarily in their relation to the cytoarchitectonic and functional organization of the underlying cortex and in their utilization as features in non-rigid registration methods. This paper presents a methodology for extracting parametric representations of the cerebral sulci from magnetic resonance images. The proposed methodology is based on deformable models utilizing characteristics of the cortical shape. Specifically, a parametric representation of a sulcus is determined by the motion of an active contour along the medial surface of the corresponding cortical fold. The active contour is initialized along the outer boundary of the brain and deforms toward the deep root of a sulcus under the influence of an external force field, restricting it to lie along the medial surface of the particular cortical fold. A parametric representation of the medial surface of the sulcus is obtained as the active contour traverses the sulcus. Based on the first fundamental form of this representation, the location and degree of an interruption of a sulcus can be readily quantified; based on its second fundamental form, shape properties of the sulcus can be determined. This methodology is tested on magnetic resonance images and it is applied to three medical imaging problems: quantitative morphological analysis of the central sulcus; mapping of functional activation along the primary motor cortex and non-rigid registration of brain images.     

Sadness enhances the experience of pain and affects pain-evoked cortical activities: an MEG study

Pain is a multidimensional phenomenon. Previous psychological studies have shown that a person's subjective pain threshold can change when certain emotions are recognized. We examined this association with magnetoencephalography. Magnetic field strength was recorded with a 306-channel neuromagnetometer while 19 healthy subjects (7 female, 12 male; age range = 20-30 years) experienced pain stimuli in different emotional contexts induced by the presentation of sad, happy, or neutral facial stimuli. Subjects also rated their subjective pain intensity. We hypothesized that pain stimuli were affected by sadness induced by facial recognition. We found: 1) the intensity of subjective pain ratings increased in the sad emotional context compared to the happy and the neutral contexts, and 2) event-related desynchronization of lower beta bands in the right hemisphere after pain stimuli was larger in the sad emotional condition than in the happy emotional condition. Previous studies have shown that event-related desynchronization in these bands could be consistently observed over the primary somatosensory cortex. These findings suggest that sadness can modulate neural responses to pain stimuli, and that brain processing of pain stimuli had already been affected, at the level of the primary somatosensory cortex, which is critical for sensory processing of pain. PERSPECTIVE: We found that subjective pain ratings and cortical beta rhythms after pain stimuli are influenced by the sad emotional context. These results may contribute to understanding the broader relationship between pain and negative emotion.     

播散性皮层抑制猫脑模型的磁共振功能成像研究

目的：用血氧水平依赖性磁共振功能成像（BOLD-fMRI）技术研究KCl诱导猫脑模型的播散性皮层抑制（SCD）。方法：雌猫6只,双侧额窦开颅并充填琼脂糖凝胶,两侧上侧裂回上方开颅,以备分别注入KCl凝胶和生理盐水凝胶。在1.5T场强下分别行静息期、KCl刺激期及对侧生理盐水刺激期BOLD-fMRI采集。结果：KCl刺激区上侧裂回及邻近边缘回脑皮层可见脑功能激活区者5例,并播及到同侧边缘回后部,而对侧大脑半球均无脑激活区。感兴趣区时间-信号强度曲线显示注入KCl后,fMRI信号立即显著增高,并维持于高水平较长时间。结论：BOLD-fMRI能准确检测出SCD伴发的神经血管耦联现象—脑血流量的变化,是研究SCD的可靠方法。     

An MEG signature corresponding to an axiomatic model of reward prediction error

Optimal decision-making is guided by evaluating the outcomes of previous decisions. Prediction errors are theoretical teaching signals which integrate two features of an outcome: its inherent value and prior expectation of its occurrence. To uncover the magnetic signature of prediction errors in the human brain we acquired magnetoencephalographic (MEG) data while participants performed a gambling task. Our primary objective was to use formal criteria, based upon an axiomatic model (Caplin and Dean, 2008a), to determine the presence and timing profile of MEG signals that express prediction errors. We report analyses at the sensor level, implemented in SPM8, time locked to outcome onset. We identified, for the first time, a MEG signature of prediction error, which emerged approximately 320 ms after an outcome and expressed as an interaction between outcome valence and probability. This signal followed earlier, separate signals for outcome valence and probability, which emerged approximately 200 ms after an outcome. Strikingly, the time course of the prediction error signal, as well as the early valence signal, resembled the Feedback-Related Negativity (FRN). In simultaneously acquired EEG data we obtained a robust FRN, but the win and loss signals that comprised this difference wave did not comply with the axiomatic model. Our findings motivate an explicit examination of the critical issue of timing embodied in computational models of prediction errors as seen in human electrophysiological data.     

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

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

The coupled dipole model: an integrated model for multiple MEG/EEG data sets

Often MEG/EEG is measured in a few slightly different conditions to investigate the functionality of the human brain. This kind of data sets show similarities, though are different for each condition. When solving the inverse problem (IP), performing the source localization, one encounters the problem that this IP is ill-posed: constraints are necessary to solve and stabilize the solution to the IP. Moreover, a substantial amount of data is needed to avoid a signal to noise ratio (SNR) that is too poor for source localizations. In the case of similar conditions, this common information can be exploited by analyzing the data sets simultaneously. The here proposed coupled dipole model (CDM) provides an integrated method in which these similarities between conditions are used to solve and stabilize the inverse problem. The coupled dipole model is applicable when data sets contain common sources or common source time functions. The coupled dipole model uses a set of common sources and a set of common source time functions (STFs) to model all conditions in one single model. The data of each condition are mathematically described as a linear combination of these common spatial and common temporal components. This linear combination is specified in a coupling matrix for each data set. The coupled dipole model was applied in two simulation studies and in one experimental study. The simulations show that the errors in the estimated spatial and temporal parameters decrease compared to the standard separate analyses. A decrease in position error of a factor of 10 was shown for the localization of two nearby sources. In the experimental application, the coupled dipole model was shown to be necessary to obtain a plausible solution in at least 3 of 15 conditions investigated. Moreover, using the CDM, a direct comparison between parameters in different conditions is possible, whereas in separate models, the scaling of the amplitude parameters varies in general from data set to data set.     

Localization of correlated network activity at the cortical level with MEG

In both hemodynamic and neurophysiological imaging methods, analysis of functionally interconnected networks has typically focused on brain areas that show strong activation in specific tasks. Alternatively, connectivity measures may be used directly to localize network nodes, independent of their level of activation. This approach requires initial cortical reference areas which may be identified based on their high level of activation, their coherence with an external reference signal, or their strong connectivity with other brain areas. Irrespective of how the nodes have been localized the mathematical complexity of the analysis methods precludes verification of the accuracy and completeness of the network structure by direct comparison with the measured data. Therefore, it is critical to understand how the choices of parameters and procedures used in the analysis affect the network identification. Here, using simulated and measured magnetoencephalography (MEG) data, and Dynamic Imaging of Coherent Sources (DICS) for connectivity analysis, we quantify the veracity of network detection at the individual and group level as a function of relevant parameter choices. Using simulations, we demonstrate that coupling measures enable accurate identification of the network structure even without external reference signals, and illustrate the applicability of this approach to real data. We show that a valid estimate of interindividual variability is critical for reliable group-level analysis. Although this study focuses on application of DICS to MEG data, many issues considered here, especially those regarding individual vs. group-level analysis, are likely to be relevant for other neuroimaging methods and analysis approaches as well.     

Simulation of cardiac pathologies using an electromechanical biventricular model and XMR interventional imaging

Simulating cardiac electromechanical activity is of great interest for a better understanding of pathologies and for therapy planning. Design and validation of such models is difficult due to the lack of clinical data. XMR systems are a new type of interventional facility in which patients can be rapidly transferred between X-ray and MR systems. Our goal is to design and validate an electromechanical model of the myocardium using XMR imaging. The proposed model is computationally fast and uses clinically observable parameters. We present the integration of anatomy, electrophysiology, and motion from patient data. Pathologies are introduced in the model and simulations are compared to measured data. Initial qualitative comparison on the two clinical cases presented is encouraging. Once fully validated, these models will make it possible to simulate different interventional strategies.     

Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neural sources using MEG

The synchronous brain activity measured via MEG (or EEG) can be interpreted as arising from a collection (possibly large) of current dipoles or sources located throughout the cortex. Estimating the number, location, and time course of these sources remains a challenging task, one that is significantly compounded by the effects of source correlations and unknown orientations and by the presence of interference from spontaneous brain activity, sensor noise, and other artifacts. This paper derives an empirical Bayesian method for addressing each of these issues in a principled fashion. The resulting algorithm guarantees descent of a cost function uniquely designed to handle unknown orientations and arbitrary correlations. Robust interference suppression is also easily incorporated. In a restricted setting, the proposed method is shown to produce theoretically zero reconstruction error estimating multiple dipoles even in the presence of strong correlations and unknown orientations, unlike a variety of existing Bayesian localization methods or common signal processing techniques such as beamforming and sLORETA. Empirical results on both simulated and real data sets verify the efficacy of this approach.     

Spatiotemporal dynamics of neural language processing: an MEG study using minimum-norm current estimates

The inferior frontal and superior temporal areas in the left hemisphere are well-known to be crucial for language processing in most right-handed individuals. This has been established by classical neurological investigations and neuropsychological studies along with metabolic brain imaging have recently revealed converging evidence. Here, we use fast neurophysiological brain imaging, magnetoencephalography (MEG), and L1 Minimum-Norm Current Estimates to investigate the time course of cortical activation underlying the magnetic Mismatch Negativity elicited by a spoken word. Left superior-temporal areas became active 136 ms after the information in the acoustic input was sufficient for identifying the word, and activation of the left inferior-frontal cortex followed after an additional delay of 22 ms. By providing answers to the where- and when-questions of cortical activation, MEG recordings paired with current estimates of the underlying cortical sources may advance our understanding of the spatiotemporal dynamics of distributed neuronal networks involved in cognitive processing in the human brain.     

Group imaging of task-related changes in cortical synchronisation using nonparametric permutation testing

Synthetic aperture magnetometry (SAM) is a nonlinear beamformer technique for producing 3D images of cortical activity from magnetoencephalography data. We have previously shown how SAM images can be spatially normalised and averaged to form a group image. In this paper we show how nonparametric permutation methods can be used to make robust statistical inference about group SAM data. Data from a biological motion direction discrimination experiment were analysed using both a nonparametric analysis toolbox (SnPM) and a conventional parametric approach utilising Gaussian field theory. In data from a group of six subjects, we were able to show robust group activation at the P < 0.05 (corrected) level using the nonparametric methods, while no significant clusters were found using the conventional parametric approach. Activation was found using SnPM in several regions of right occipital-temporal cortex, including the superior temporal sulcus, V5/MT, the fusiform gyrus, and the lateral occipital complex.