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r Human Brain Mapping 31:1512–1531 (2010) r A Parametric Empirical Bayesian Framework for fMRI-Constrained MEG/EEG Source Reconstruction
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@MISC{Henson_rhuman,
author = {Richard N. Henson and Guillaume Fl and Karl J. Friston and Jérémie Mattout},
title = {r Human Brain Mapping 31:1512–1531 (2010) r A Parametric Empirical Bayesian Framework for fMRI-Constrained MEG/EEG Source Reconstruction},
year = {}
}
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Abstract
Abstract: We describe an asymmetric approach to fMRI and MEG/EEG fusion in which fMRI data are treated as empirical priors on electromagnetic sources, such that their influence depends on the MEG/ EEG data, by virtue of maximizing the model evidence. This is important if the causes of the MEG/ EEG signals differ from those of the fMRI signal. Furthermore, each suprathreshold fMRI cluster is treated as a separate prior, which is important if fMRI data reflect neural activity arising at different times within the EEG/MEG data. We present methodological considerations when mapping from a 3D fMRI Statistical Parametric Map to a 2D cortical surface and thence to the covariance components used within our Parametric Empirical Bayesian framework. Our previous introduction of a canonical (inverse-normalized) cortical mesh also allows deployment of fMRI priors that live in a template space; for example, from a group analysis of different individuals. We evaluate the ensuing scheme with MEG and EEG data recorded simultaneously from 12 participants, using the same face-processing paradigm under which independent fMRI data were obtained. Because the fMRI priors become part of the generative model, we use the model evidence to compare (i) multiple versus single, (ii) valid versus invalid, (iii) binary versus continuous, and (iv) variance versus covariance fMRI priors. For these data, multiple, valid, binary, and variance fMRI priors proved best for a standard Minimum
Keyphrases
parametric empirical bayesian framework human brain mapping fmri-constrained meg eeg source reconstruction fmri prior model evidence fmri data different individual independent fmri data asymmetric approach neural activity eeg data suprathreshold fmri cluster present methodological consideration previous introduction separate prior meg eeg fusion cortical mesh eeg meg data cortical surface group analysis fmri signal multiple versus meg eeg signal variance versus covariance fmri prior empirical prior different time template space covariance component standard minimum variance fmri prior binary versus face-processing paradigm valid versus invalid meg eeg data fmri statistical parametric map electromagnetic source generative model
