Image Processing Reference
In-Depth Information
discussed in this subsection and shown by the results presented in the next
subsection, there are specific ranges of applications in which the linearity between
BOLD and neuronal activation can be assumed.
8.4.3
A
NALYSIS
M
ETHODS
Whenever applicable, a simple comparative analysis of the results obtained from
conventional unimodal analyses, together with findings reported elsewhere, can be
considered as the first confirmatory level of a multimodal analysis. This type of
analysis is very flexible, as long as the researcher knows how to interpret the results
and to draw useful conclusions, especially whenever the results of comparison
reveal commonalities and differences between the two [83]. On the other hand, by
default, a unimodal analysis makes limited use of the data from the modalities and
encourages researchers to look for analysis methods that would incorporate the
advantages of each single modality. Nevertheless, simple inspection is helpful for
drawing preliminary conclusions regarding the plausibility of performing a conjoint
analysis using one of the methods described in this subsection, including correlative
analysis, which might be considered an initial approach to try.
8.4.3.1
Correlative Analysis of EEG and MEG with fMRI
In some experiments, the E/MEG signal can serve as the detector of spontaneous
neuronal activity (e.g., epileptic discharges) or changes in the processing states (e.g.,
vigilance states). The time onsets derived from E/MEG are alone valuable for further
fMRI analysis, in which the BOLD signal often cannot provide such timing infor-
mation. For instance, such use of EEG data is characteristic of the experiments
performed via a triggered fMRI acquisition scheme (Subsection 8.3.2).
Correlative E/MEG/fMRI analysis becomes more intriguing if there is a
stronger belief in the linear dependency between the BOLD response and features
of E/MEG signal (e.g., amplitudes of ERP peaks, powers of frequency compo-
nents) than between the hemodynamics of the brain and the corresponding param-
eter of the design (e.g., frequency of stimulus presentation or level of stimulus
degradation). Then E/MEG/fMRI analysis effectively reduces the inherent bias
present in the conventional fMRI analysis methods by removing the possible
nonlinearity between the design parameter and the evoked neuronal response.
The correlative analysis relies on the preprocessing of E/MEG data to extract
the features of interest to be compared with the fMRI time course. The obtained
E/MEG features first get convolved with a hypothetical HRF (Subsection 8.4.2.1)
to accommodate HR sloppiness and are then subsampled to fit the temporal
resolution of fMRI. The analysis of fMRI signal correlation with amplitudes of
selected peaks of ERPs revealed sets of voxels that have a close-to-linear depen-
dency between the BOLD response and amplitude of the selected ERP peak (N170
in Horovitz et al. [64], P300 in Horovitz et al. [63], and amplitude of mismatch
negativity (MMN) [80]), thus providing a strong correlation ( [64]). A
parametric experimental design with different noise levels introduced for the
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