Integration of Functional MRI, Structural MRI, EEG, and MEG (original) (raw)
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Integration of EEG/MEG with MRI and fMRI
IEEE Engineering in Medicine and Biology Magazine, 2006
EEG and MEG are important functional neuroimaging modalities for studying the temporal dynamics of neural activities and interactions, but the accurate localization of neural activities still remains a challenging problem. Combining EEG/MEG with MRI or/and functional MRI (fMRI) holds promise to significantly increase the spatial resolution of electromagnetic source imaging, and at the same time, allows tracing the rapid neural processes and information pathways within the brain, which cannot be achieved using these modalities in isolation. In this paper, we review some recent progresses in multimodal neuroimaging, with special emphasis on the integration of EEG, MEG with MRI and fMRI. Some examples are shown to illustrate the importance of the combined source analysis in clinical and experimental studies.
An fMRI-Constrained MEG Source Analysis with Procedures for Dividing and Grouping Activation
NeuroImage, 2002
To analyze neural activity using magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), we developed a method for fixing equivalent current dipoles of MEG in activation areas of fMRI. It includes a procedure for dividing large fMRI activation volumes into subvolumes in each of which a dipole is placed and another procedure for grouping neighboring dipoles whose temporal changes are inseparable based on MEG data. To optimize the procedures' parameters, we carried out simulations and found that (1) any single dipole within 10 mm from a true source can explain MEG data with a correlation of 94% on average for the low signal-to-noise ratio of 3 and (2) a neighboring dipole within a few tens of millimeters from the dipole nearest to the true source tends to be highly incorporated in explaining MEG data. We applied the method to data measured in a language experiment and detected 13 significant sources. The results show that the present method is promising for detecting neural activity originating from a number of separate neural sources. © 2002 Elsevier Science (USA)
Multimodal functional imaging using fMRI-informed regional EEG/MEG source estimation
NeuroImage, 2010
We propose a novel method, fMRI-Informed Regional Estimation (FIRE), which utilizes information from fMRI in E/MEG source reconstruction. FIRE takes advantage of the spatial alignment between the neural and the vascular activities, while allowing for substantial differences in their dynamics. Furthermore, with a region-based approach, FIRE estimates the model parameters for each region independently. Hence, it can be efficiently applied on a dense grid of source locations. The optimization procedure at the core of FIRE is related to the re-weighted minimum-norm algorithms. The weights in the proposed approach are computed from both the current source estimates and fMRI data, leading to robust estimates in the presence of silent sources in either fMRI or E/MEG measurements. We employ a Monte Carlo evaluation procedure to compare the proposed method to several other joint E/MEG-fMRI algorithms. Our results show that FIRE provides the best trade-off in estimation accuracy between the spatial and the temporal accuracy. Analysis using human E/MEG-fMRI data reveals that FIRE significantly reduces the ambiguities in source localization present in the minimum-norm estimates, and that it accurately captures activation timing in adjacent functional regions.
A Framework for the Integration of fMRI, sMRI, EEG, and MEG
While fMRI (functional Magnetic Resonance Imaging) yields high spatial resolution, brain dynamics are hardly resolved. This is due to two facts: First, there is a trade-off between SNR (Signal-to-Noise Ratio), spatial, and temporal resolution of the imaging process. Secondly, there is an inherent low-pass filter in the event chain that leads from neuronal activity to hemodynamic reactions as they are measured. The combination with EEG (Electroencephalography) and MEG (Magnetoencephalography) source reconstruction techniques promises to add the desired temporal resolution. EEG and MEG sampling times are usually in the order of a millisecond, and the effects of the neuronal activity that are measured (electric potentials and magnetic fields) are of an instantaneous nature. They can be modeled using the quasi-static approximations of Maxwell’s equations. However, the localizing power of EEG or MEG alone is limited and typically in the order of one centimeter. To achieve a combination o...
Multimodal Integration: fMRI, MRI, EEG, MEG
2005
This chapter provides a comprehensive survey of the motivations, assumptions and pitfalls associated with combining signals such as fMRI with EEG or MEG. Our initial focus in the chapter concerns mathematical approaches for solving the localization problem in EEG and MEG. Next we document the most recent and promising ways in which these signals can be combined with fMRI. Specifically, we look at correlative analysis, decomposition techniques, equivalent dipole fitting, distributed sources modeling, beamforming, and Bayesian methods. Due to difficulties in assessing ground truth of a combined signal in any realistic experiment-a difficulty further confounded by lack of accurate biophysical models of BOLD signal-we are cautious to be optimistic about multimodal integration. Nonetheless, as we highlight and explore the technical and methodological difficulties of fusing heterogeneous signals, it seems likely that correct fusion of multimodal data will allow previously inaccessible spatiotemporal structures to be visualized and formalized and thus eventually become a useful tool in brain imaging research.
Mapping human brain function with MEG and EEG: methods and validation
NeuroImage, 2004
We survey the field of magnetoencephalography (MEG) and electroencephalography (EEG) source estimation. These modalities offer the potential for functional brain mapping with temporal resolution in the millisecond range. However, the limited number of spatial measurements and the ill-posedness of the inverse problem present significant limits to our ability to produce accurate spatial maps from these data without imposing major restrictions on the form of the inverse solution. Here we describe approaches to solving the forward problem of computing the mapping from putative inverse solutions into the data space. We then describe the inverse problem in terms of low dimensional solutions, based on the equivalent current dipole (ECD), and high dimensional solutions, in which images of neural activation are constrained to the cerebral cortex. We also address the issue of objective assessment of the relative performance of inverse procedures by the free-response receiver operating characteristic (FROC) curve. We conclude with a discussion of methods for assessing statistical significance of experimental results through use of the bootstrap for determining confidence regions in dipole-fitting methods, and random field (RF) and permutation methods for detecting significant activation in cortically constrained imaging studies.
Multimodal integration of EEG, MEG and fMRI data for the solution of the neuroimage puzzle
Magnetic Resonance Imaging, 2004
In this paper, advanced methods for the modeling of human cortical activity from combined high-resolution electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) data are presented. These methods include a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from magnetic resonance images, multidipole source model and regularized linear inverse source estimates of cortical current density. Determination of the priors in the resolution of the linear inverse problem was performed with the use of information from the hemodynamic responses of the cortical areas as revealed by blockdesigned (strength of activated voxels) fMRI. Examples of the application of these methods to the estimation of the time varying cortical current density activity in selected region of interest (ROI) are presented for movement-related high-resolution EEG data.
Functional magnetic resonance imaging of the human brain: Data acquisition and analysis
Experimental Brain Research, 1998
It is now feasible to create spatial maps of activity in the human brain completely non-invasively using magnetic resonance imaging. Magnetic resonance imaging (MRI) images in which the spin magnetization is refocussed by gradient switching are sensitive to local changes in magnetic susceptibility, which can occur when the oxygenation state of blood changes. Cortical neural activity causes increases in blood flow, which usually result in changes in blood oxygenation. Hence changes of image intensity can be observed, given rise to the socalled Blood Oxygenation Level Dependent (BOLD) contrast technique. Use of echo-planar imaging methods (EPI) allows the monitoring over the entire brain of such changes in real time. A temporal resolution of 1±3 s, and a spatial resolution of 2 mm in-plane, can thus be obtained. Generally in a brain mapping experiment hundred of brain image volumes are acquired at repeat times of 1±6 s, while brain tasks are performed. The data are transformed into statistical maps of image difference, using the technique known as statistical parametric mapping (SPM). This method, based on robust multilinear regression techniques, has become the method of reference for analysis of positron emission tomography (PET) image data. The special characteristics of functional MRI data require some modification of SPM algorithms and strategies, and the MRI data must be gaussianized in time and space to conform to the assumptions of the statistics of Gaussian random fields. The steps of analysis comprise: removal of head movement effects, spatial smoothing, and statistical interference, which includes temporal smoothing and removal by fitting of temporal variations slower than the experimental paradigm. By these means, activation maps can be generated with great flexibility and statistical power, giving probability estimates for activated brain regions based on intensity or spatial extent, or both combined. Recent studies have shown that patterns of activation obtained in human brain for a given stimulus are indepen-dent of the order and spatial orientation with which MRI images are acquired, and hence that inflow effects are not important for EPI data with a TR much longer than T1.
Neural plasticity, 2016
Understanding the mechanism of neuroplasticity is the first step in treating neuromuscular system impairments with cognitive rehabilitation approaches. To characterize the dynamics of the neural networks and the underlying neuroplasticity of the central motor system, neuroimaging tools with high spatial and temporal accuracy are desirable. EEG and fMRI stand among the most popular noninvasive neuroimaging modalities with complementary features, yet achieving both high spatial and temporal accuracy remains a challenge. A novel multimodal EEG/fMRI integration method was developed in this study to achieve high spatiotemporal accuracy by employing the most probable fMRI spatial subsets to guide EEG source localization in a time-variant fashion. In comparison with the traditional fMRI constrained EEG source imaging method in a visual/motor activation task study, the proposed method demonstrated superior localization accuracy with lower variation and identified neural activity patterns th...