Multimodal Integration: fMRI, MRI, EEG, and MEG - Advance Image Processing in Magnetic Resonance Imaging - page 246

dipole localization problem and could be augmented to accept fMRI data if the
generative model were augmented to produce it.
FMRI-conditioned E/MEG DECD methods have been shown to be a rela-
tively simple and mathematically grounded for source imaging when there is
good spatial agreement between E/MEG and fMRI signals. Due to the advantages
of such methods, it might be valuable to consider other advanced E/MEG DECD
methods such as FOCUSS [159], which is known to bring improvement of
estimation of focal sources over simple linear inverse methods [160].
ICA as a signal decomposition technique has been found effective in removing
artifacts in E/MEG without degrading neuronal signals [161-164], and moreover is
known to be superior to PCA in the component analysis of E/MEG signals [165].
Initial research using ICA of fMRI in the spatial domain [166] was controversial;
however, consecutive experiments and generalization of ICA to fMRI in the temporal

domain (see Calhoun et al. [ 167 ] for an overview) has increased its normative value.

The development of ICA methods for the analysis of multimodal data provides a
logical extension of the decomposition techniques covered earlier in the chapter.
Because most of the multimodal methods presented in this chapter rely upon
the linear dependence between signals, it is important to analyze, expand, and
formalize the knowledge about the “linear” case. The formulation of a general
BOLD signal model capable of describing the desired nonlinear dependency in
terms of neuronal activation and nuisance physiological parameters would con-
stitute a major step toward the development of multimodal methods with a wider
range of application than in the current linear domain. Without such a model and
without valid estimates of the underlying physiological parameters involved in
the model, any multimodal analysis can not be considered progress.
In sum, it seems clear that f MRI should serve as a complementary evidence
factor, rather than a hard constraint, in E/MEG source localization methods.
The preprocessing of both fMRI and E/MEG signals should be done in order to
select features of interest which had been previously reported to have good
agreement between the two modalities. Any multimodal experiment should be
based on the comparative study of unimodal experiments and analyses that show
good agreement before performing conjoint data analysis.

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