fMRI–EEG integrated cortical source imaging by use of time-variant spatial constraints

Abstract

In response to the need of establishing a high-resolution spatiotemporal neuroimaging technique, tremendous efforts have been focused on developing multimodal strategies that combine the complementary advantages of high-spatial-resolution functional magnetic resonance imaging (fMRI) and high-temporal-resolution electroencephalography (EEG) or magnetoencephalography (MEG). A critical challenge to the fMRI-EEG/MEG integration lies in the spatial mismatches between fMRI activations and instantaneous electrical source activities. Such mismatches are fundamentally due to the fact that fMRI and EEG/MEG signals are generated and collected in highly different time scales. In this paper, we propose a new theoretical framework to solve the problem of fMRI-EEG integrated cortical source imaging. The new framework has two principal technical advancements. First, by assuming a linear neurovascular coupling, a method is derived to quantify the fMRI signal in each voxel as proportional to the time integral of the power of local electrical current during the period of event related potentials (ERP). Second, the EEG inverse problem is solved for every time instant using an adaptive Wiener filter, in which the prior time-variant source covariance matrix is estimated based on combining the quantified fMRI responses and the segmented EEG signals before response averaging. A series of computer simulations were conducted to evaluate the proposed methods in terms of imaging the instantaneous cortical current density (CCD) distribution and estimating the source time courses with a millisecond temporal resolution. As shown in the simulation results, the instantaneous CCD reconstruction by using the proposed fMRI-EEG integration method was robust against both fMRI false positives and false negatives while retaining a spatial resolution nearly as high as that of fMRI. The proposed method could also reliably estimate the source waveforms when multiple sources were temporally correlated or uncorrelated, or were sustained or transient, or had some features in frequency or phase, or had even more complicated temporal dynamics. Moreover, applying the proposed method to real fMRI and EEG data acquired in a visual experiment yielded a time series of reconstructed CCD images, in agreement with the traditional view of hierarchical visual processing. In conclusion, the proposed method provides a reliable technique for the fMRI-EEG integration and represents a significant advancement over the conventional fMRI-weighted EEG (or MEG) source imaging techniques, and is also applicable to the fMRI-MEG integrated source imaging.

Spatial Correspondence Between Functional MRI (fMRI) Activations and Cortical Current Density Maps of Event-Related Potentials (ERP): A Study with Four Tasks

“…The presence of a significant spatial correspondence is concordant with the fact that event-related electrical activity originates from the same areas for which a heamodynamic response is detected, and with the fact that the EEG and BOLD signals, although very different, are both representative of local synaptic activity (Liu and He 2008). Finding a significant spatial correspondence despite the very different spatial and temporal resolutions of the two modalities is a prerequisite for their direct integration, enabling to combine the high spatial resolution of fMRI with the high temporal resolution of ERPs.…”

“…In a recent article, it was demonstrated that the amplitude of the BOLD response can be predicted using a linear model based on the time-integral of the power of local event-related synaptic activity (Liu and He 2008). Complete correspondence, however, is not expected.…”

“…Rather than preselecting specific ERP components or time-windows of interest, we investigated the correspondence between fMRI activations and RMS cortical current density computed over the whole post-stimulus epoching windows (0-500 ms for visual and auditory stimulation tasks and 0-1 s for the image and word recognition tasks), corresponding to the time-integral of all event-related electric activity (Liu and He 2008).…”

A weighted small world network measure for assessing functional connectivity

“…Furthermore, Laplacian transformed data emphasize more cortical activities and suppress contributions from deep brain structures. Further research can be extended to estimating the cortical current density or volume current density distribution from the scalp EEG (Liu and He, 2008) by solving the inverse problem, and then applying the network measures, which would allow for a stronger inference of the inter-regional connectivity in the source domain. Such approaches could also be combined with inverse approaches better suited to modeling deeper sources such as the ACC.…”

The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging

“…Source space projection can be done with commonly used software packages (e.g., the open-source Matlab toolbox FieldTrip or BrainStorm or the commercial products BrainVoyager or Besa) by inverting a forward solution on the basis of given source dipole positions and orientations and a volume conductor model; all of the above can be individually estimated from anatomical MRI data of subjects (Chan et al, 2009 He and Liu, 2008;Liu and He, 2008;Michel et al, 2004;Pascual-Marqui, 1999)]. …”