Localizing True Brain Interactions from EEG and MEG Data with Subspace Methods and Modified Beamformers (original) (raw)
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PLoS ONE, 2014
Electroencephalography (EEG) and magnetoencephalography (MEG) are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis. In the present study, the cortical and sub-cortical network of coherent sources at the finger tapping task frequency (2-4 Hz) and the modes of interaction within this network were analysed in 15 healthy subjects using a beamformer approach called the dynamic imaging of coherent sources (DICS) with subsequent source signal reconstruction and renormalized partial directed coherence analysis (RPDC). MEG and EEG data were recorded simultaneously allowing the comparison of each of the modalities separately to that of the combined approach. We found the identified network of coherent sources for the finger tapping task as described in earlier studies when using only the MEG or combined MEG+EEG whereas the EEG data alone failed to detect single sub-cortical sources. The signal-to-noise ratio (SNR) level of the coherent rhythmic activity at the tapping frequency in MEG and combined MEG+EEG data was significantly higher than EEG alone. The functional connectivity analysis revealed that the combined approach had more active connections compared to either of the modalities during the finger tapping (FT) task. These results indicate that MEG is superior in the detection of deep coherent sources and that the SNR seems to be more vital than the sensitivity to theoretical dipole orientation and the volume conduction effect in the case of EEG.
Source connectivity analysis with MEG and EEG
Human Brain Mapping, 2009
Interactions between functionally specialized brain regions are crucial for normal brain function. Magnetoencephalography (MEG) and electroencephalography (EEG) are techniques suited to capture these interactions, because they provide whole head measurements of brain activity in the millisecond range. More than one sensor picks up the activity of an underlying source. This field spread severely limits the utility of connectivity measures computed directly between sensor recordings. Consequentially, neuronal interactions should be studied on the level of the reconstructed sources. This article reviews several methods that have been applied to investigate interactions between brain regions in source space. We will mainly focus on the different measures used to quantify connectivity, and on the different strategies adopted to identify regions of interest. Despite various successful accounts of MEG and EEG source connectivity, caution with respect to the interpretation of the results is still warranted. This is due to the fact that effects of field spread can never be completely abolished in source space. However, in this very exciting and developing field of research this cautionary note should not discourage researchers from further investigation into the connectivity between neuronal sources. Hum
NeuroImage, 2008
In understanding and modeling brain functioning by EEG/MEG, it is not only important to be able to identify active areas but also to understand interference among different areas. The EEG/MEG signals result from the superimposition of underlying brain source activities volume conducted through the head. The effects of volume conduction produce spurious interactions in the measured signals. It is fundamental to separate true source interactions from noise and to unmix the contribution of different systems composed by interacting sources in order to understand interference mechanisms. As a prerequisite, we consider the problem of unmixing the contribution of uncorrelated sources to a measured field. This problem is equivalent to the problem of unmixing the contribution of different uncorrelated compound systems composed by interacting sources. To this end, we develop a principal component analysis-based method, namely, the source principal component analysis (sPCA), which exploits the underlying assumption of orthogonality for sources, estimated from linear inverse methods, for the extraction of essential features in signal space. We then consider the problem of demixing the contribution of correlated sources that comprise each of the compound systems identified by using sPCA. While the sPCA orthogonality assumption is sufficient to separate uncorrelated systems, it cannot separate the individual components within each system. To address that problem, we introduce the Minimum Overlap Component Analysis (MOCA), employing a pure spatial criterion to unmix pairs of correlates (or coherent) sources. The proposed methods are tested in simulations and applied to EEG data from human µ and α rhythms.
NeuroImage, 2014
We introduce a novel method to estimate bivariate synchronization, i.e. interacting brain sources at a specific frequency or band, from MEG or EEG data robust to artifacts of volume conduction. The data driven calculation is solely based on the imaginary part of the cross-spectrum as opposed to the imaginary part of coherency. In principle, the method quantifies how strong a synchronization between a distinct pair of brain sources is present in the data. As an input of the method all pairs of pre-defined locations inside the brain can be used which is computationally exhaustive. In contrast to that, reference sources can be used that have been identified by any source reconstruction technique in a prior analysis step. We introduce different variants of the method and evaluate the performance in simulations. As a particular advantage of the proposed methodology, we demonstrate that the novel approach is capable of investigating differences in brain source interactions between experim...
Source-space ICA for EEG source separation, localization, and time-course reconstruction
NeuroImage, 2014
We propose source-space independent component analysis (ICA) for separation, tomography, and time-course reconstruction of EEG and MEG source signals. Source-space ICA is based on the application of singular value decomposition and ICA on the neuroelectrical signals from all brain voxels obtained post minimum-variance beamforming of sensor-space EEG or MEG. We describe the theoretical background and equations, then evaluate the performance of this technique in several different situations, including weak sources, bilateral correlated sources, multiple sources, and cluster sources. In this approach, tomographic maps of sources are obtained by back-projection of the ICA mixing coefficients into the source-space (3-D brain template). The advantages of source-space ICA over the popular alternative approaches of sensor-space ICA together with dipole fitting and power mapping via minimum-variance beamforming are demonstrated. Simulated EEG data were produced by forward head modeling to pr...
Journal of Biomedical Physics and Engineering, 2018
Background: Brain source imaging based on electroencephalogram (EEG) data aims to recover the neuron populations’ activity producing the scalp potentials. This procedure is known as the EEG inverse problem. Recently, beamformers have gained a lot of consideration in the EEG inverse problem.Objective: Beamformers lack acceptable performance in the case of correlated brain sources. These sources happen when some regions of the brain have simultaneous or correlated activities such as auditory stimulation or moving left and right extremities of the body at the same time. In this paper, we have developed a multichannel beamformer robust to correlated sources. Material and Methods: We have looked at the problem of brain source imaging and beamforming from a blind source separation point of view. We focused on the spatially constraint independent component analysis (scICA) algorithm, which generally benefits from the pre-known partial information of mixing matrix, and modified the steps of...
Estimating true brain connectivity from EEG/MEG data invariant to coordinate transformations
Neuroscience Letters, 2011
The imaginary part of coherency is a measure to investigate the synchronization of brain sources on the EEG/MEG sensor level, robust to artifacts of volume conduction meaning that independent sources cannot generate a signicant result. It does not mean, however, that volume conduction is irrelevant when true interactions are present. Here, we analyze in detail the possibilities to construct measures of true brain interactions which are strictly invariant to linear spatial transformations of the sensor data. Specically, such measures can be constructed from maximization of imaginary coherency in virtual channels, bivariate measures as a corrected variate of imaginary coherence, and global measures indicating the total interaction contained within a space or between two spaces. A complete theoretic framework on this question is provided for second order statistical moments. Relations to existing linear and nonlinear approaches are presented. We applied the methods to resting state EEG data, showing clear interactions at all bands, and to a combined measurement of EEG and MEG during rest condition and a nger tapping task. We found that MEG was capable of observing brain interactions which were not observable in the EEG data.
NeuroImage, 2012
The imaginary part of coherency is a measure to investigate the synchronization of brain sources on the EEG/MEG sensor level, robust to artifacts of volume conduction meaning that independent sources cannot generate a signicant result. It does not mean, however, that volume conduction is irrelevant when true interactions are present. Here, we analyze in detail the possibilities to construct measures of true brain interactions which are strictly invariant to linear spatial transformations of the sensor data. Specically, such measures can be constructed from maximization of imaginary coherency in virtual channels, bivariate measures as a corrected variate of imaginary coherence, and global measures indicating the total interaction contained within a space or between two spaces. A complete theoretic framework on this question is provided for second order statistical moments. Relations to existing linear and nonlinear approaches are presented. We applied the methods to resting state EEG data, showing clear interactions at all bands, and to a combined measurement of EEG and MEG during rest condition and a nger tapping task. We found that MEG was capable of observing brain interactions which were not observable in the EEG data.
Localization of coherent sources by simultaneous MEG and EEG beamformer
Medical & Biological Engineering & Computing, 2013
Simultaneous magnetoencephalography (MEG) and electroencephalography (EEG) analysis is known generally to yield better localization performance than a single modality only. For simultaneous analysis, MEG and EEG data should be combined to maximize synergistic effects. Recently beamformer for simultaneous MEG/EEG analysis was proposed to localize both radial and tangential components well, while single-modality analyses could not detect them, or had relatively higher location-bias. In practice, most interesting brain sources are likely to be activated coherently; however, conventional beamformer may not work properly for such coherent sources. To overcome this difficulty, a linearly-constrained minimum variance (LCMV) beamformer may be used with a source suppression strategy. In this work, simultaneous MEG/EEG LCMV beamformer using source suppression was formulated firstly to investigate its capability over various suppression strategies.