Multimodal functional imaging using fMRI-informed regional EEG/MEG source estimation

Abstract

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.

MEG Source Localization of Spatially Extended Generators of Epileptic Activity: Comparing Entropic and Hierarchical Bayesian Approaches

“…Several studies have been done on reconstructing extended cortical sources based on distributed model using EEG or MEG data [31], [36], [54]. Other studies have been proposed in the context of extended cortical patches and beamformer approaches; these methods are extensions or generalizations of dipole scanning approaches [11], [55]–[61].…”

“…Few studies demonstrated how introducing parceling of the brain (obtained from some anatomical atlases) was quite useful to better condition the inverse problem [29], [31], [36], [66]. Similarly, Lapalme et al [28] used a data driven parceling technique to partition the whole cortical surface into functionally homogenous parcels.…”

“…Characterizing brain activity, assuming functional homogeneity within brain parcels has proved to be an efficient approach to analyze neuroimaging data, either in EEG/MEG [16], [28]–[31], in fMRI [32]–[34] or in multimodal fusion [35], [36].…”

How to use fMRI functional localizers to improve EEG/MEG source estimation

“…In Henson et al, (2010), each fMRI cluster is treated as an independent prior whose pertinence for source reconstruction is evaluated using a parametric empirical Bayesian framework. In Ou et al, (2010), the source weights are evaluated from both the fMRI and EEG/MEG data using a re-weighted minimum-norm algorithm. While these different techniques have proven to be accurate on both synthetic and real data, their use is nonetheless complicated at the scale of group studies, as they require a new fMRI acquisition every time a change is introduced in the stimulus.…”

Electrophysiological Imaging of Brain Activity and Connectivity—Challenges and Opportunities

“…Thus the mismatch between a single static fMRI map and consecutive snapshots of EEG/MEG during the same period can lead to biased estimates as the fMRI extra sources (seen in fMRI but not EEG/MEG), the fMRI invisible sources (seen in EEG/MEG but not in fMRI), and the displacement sources (detailed discussion in [5]). New methods have been proposed towards overcoming this limitation, by means of a time-variant spatial constraint estimated from a combination of quantified fMRI and EEG responses [65] or estimating regionally fMRI-informed models by allowing model parameters jointly computed from electrophysiological source estimates and fMRI data rather than exclusively dependent on fMRI [66]. Examples of applying EEG/MEG-fMRI integration in the investigation of visual processing function have demonstrated how the subtle spatiotemporal dynamics revealed from electrophysiological imaging were able to delineate the hypotheses in regard to the underlying neural processes [59, 65, 67].…”