Magnetoencephalography Source Imaging Research Articles

On the accuracy of boundary element models in MEG source imaging

On the accuracy of boundary element models in MEG source imaging

Long range high-gamma (60-90 Hz) coupling between primary motor cortex and SMA during motor control revealed by MEG source imaging

Long range high-gamma (60-90 Hz) coupling between primary motor cortex and SMA during motor control revealed by MEG source imaging

MEG source imaging reveals a network of high-frequency activity in an attentive listening task

MEG source imaging reveals a network of high-frequency activity in an attentive listening task

EEG/MEG Source Imaging: Methods, Challenges, and Open Issues

We present the four key areas of research—preprocessing, the volume conductor, the forward problem, and the inverse problem—that affect the performance of EEG and MEG source imaging. In each key area we identify prominent approaches and methodologies that have open issues warranting further investigation within the community, challenges associated with certain techniques, and algorithms necessitating clarification of their implications. More than providing definitive answers we aim to identify important open issues in the quest of source localization.

Linear constraint minimum variance beamformer functional magnetic resonance inverse imaging

Accurate estimation of the timing of neural activity is required to fully model the information flow among functionally specialized regions whose joint activity underlies perception, cognition and action. Attempts to detect the fine temporal structure of task-related activity would benefit from functional imaging methods allowing higher sampling rates. Spatial filtering techniques have been used in magnetoencephalography source imaging applications. In this work, we use the linear constraint minimal variance (LCMV) beamformer localization method to reconstruct single-shot volumetric functional magnetic resonance imaging (fMRI) data using signals acquired simultaneously from all channels of a high density radio-frequency (RF) coil array. The LCMV beamformer method generalizes the existing volumetric magnetic resonance inverse imaging (InI) technique, achieving higher detection sensitivity while maintaining whole-brain spatial coverage and 100 ms temporal resolution. In this paper, we begin by introducing the LCMV reconstruction formulation and then quantitatively assess its performance using both simulated and empirical data. To demonstrate the sensitivity and inter-subject reliability of volumetric LCMV InI, we employ an event-related design to probe the spatial and temporal properties of task-related hemodynamic signal modulations in primary visual cortex. Compared to minimum-norm estimate (MNE) reconstructions, LCMV offers better localization accuracy and superior detection sensitivity. Robust results from both single subject and group analyses demonstrate the excellent sensitivity and specificity of volumetric InI in detecting the spatial and temporal structure of task-related brain activity.

Event-related single-shot volumetric functional magnetic resonance inverse imaging of visual processing

Developments in multi-channel radio-frequency (RF) coil array technology have enabled functional magnetic resonance imaging (fMRI) with higher degrees of spatial and temporal resolution. While modest improvement in temporal acceleration has been achieved by increasing the number of RF coils, the maximum attainable acceleration in parallel MRI acqisition is intrinsically limited only by the amount of independent spatial information in the combined array channels. Since the geometric configuration of a large- n MRI head coil array is similar to that used in EEG electrode or MEG SQUID sensor arrays, the source localization algorithms used in MEG or EEG source imaging can be extended to also process MRI coil array data, resulting in greatly improved temporal resolution by minimizing k-space traversal during signal acquisition. Using a novel approach, we acquire multi-channel MRI head coil array data and then apply inverse reconstruction methods to obtain volumetric fMRI estimates of blood oxygenation level dependent (BOLD) contrast at unprecedented whole-brain acquisition rates of 100 ms. We call this combination of techniques magnetic resonance Inverse Imaging (InI), a method that provides estimates of dynamic spatially-resolved signal change that can be used to construct statistical maps of task-related brain activity. We demonstrate the sensitivity and inter-subject reliability of volumetric InI using an event-related design to probe the hemodynamic signal modulations in primary visual cortex. Robust results from both single subject and group analyses demonstrate the sensitivity and feasibility of using volumetric InI in high temporal resolution investigations of human brain function.

Sparse source imaging in electroencephalography with accurate field modeling

We have developed a new L1-norm based generalized minimum norm estimate (GMNE) and have fully characterized the concept of sparseness regularization inherited in the proposed algorithm, which is termed as sparse source imaging (SSI). The new SSI algorithm corrects inaccurate source field modeling in previously reported L1-norm GMNEs and proposes that sparseness a priori should only be applied to the regularization term, not to the data term in the formulation of the regularized inverse problem. A new solver to the newly developed SSI has been adopted and known as the second-order cone programming. The new SSI is assessed by a series of simulations and then evaluated using somatosensory evoked potential (SEP) data with both scalp and subdural recordings in a human subject. The performance of SSI is compared with other L1-norm GMNEs and L2-norm GMNEs using three evaluation metrics, i.e., localization error, orientation error, and strength percentage. The present simulation results indicate that the new SSI has significantly improved performance in all evaluation metrics, especially in the metric of orientation error. L2-norm GMNEs show large orientation errors because of the smooth regularization. The previously reported L1-norm GMNEs show large orientation errors due to the inaccurate source field modeling. The SEP source imaging results indicate that SSI has the best accuracy in the prediction of subdural potential field as validated by direct subdural recordings. The new SSI algorithm is also applicable to MEG source imaging.

FMRI‐constrained MEG source imaging and consideration of fMRI invisible sources

Recent studies on multimodal brain source imaging have shown that the use of functional MRI (fMRI) prior information could enhance spatial resolution of magnetoencephalography (MEG), while MEG could compensate poor temporal resolution of fMRI. This article deals with a multimodal imaging method, which combines fMRI and MEG for enhancing both spatial and temporal resolutions. Recent studies on the combination of fMRI and MEG have suggested that the fMRI prior information could be very easily implemented by just giving different weighting factors to the diagonal terms of source covariance matrix in linear inverse operator. We applied the fMRI constrained imaging method to several simulation data and experimental data (Japanese language lexical judgment experiment), and found that some MEG sources may be eliminated by the introduction of the fMRI weighting and the eliminated sources may affect source estimation in fMRI activation regions. In this article, in order to check whether the eliminated sources were fMRI invisible ones or just spurious ones, we placed small numbers of regional sources (rotating dipoles) around all possible activation regions and investigated their temporal changes. By investigating the results carefully, we could evaluate whether the missed sources were real or not.

Long-term follow up of cardia intestinal metaplasia (CIM) and cardia dysplasia

Magnetoencephalography (MEG) is used in clinical and fundamental studies of brain functions, primarily for the excellent temporal resolution it provides. The spatial resolution is often assumed to be poor, because of the ill-posed nature of MEG source modeling. However, the question of spatial resolution in MEG has seldom been studied in quantitative detail. Here we use the well-known retinotopic organization of the primary visual cortex (V1) as a benchmark for estimating the spatial resolution of MEG source imaging. Using a standard visual stimulation paradigm in human subjects, we find that individual MEG sources exhibit well-delineated visual receptive fields that collectively follow the known mapping of the retinal surface onto the cortex. Based on the size of these receptive fields and the variability of the signal, we are able to resolve MEG signals separated by approximately 7 mm in smooth regions of cortex and less than 1 mm for signals near curved gyri. The maximum resolution is thus comparable to that of the spacing of hypercolumns in human visual cortex. Overall, our results suggest that the spatial resolution of MEG can approach or in some cases exceed that of fMRI.