Image Processing Reference
In-Depth Information
stimulus degradation [64,80] or different levels of sound frequency deviant [80]
helped to extend the range of detected ERP and fMRI activations, thus effectively
increasing the significance of the results found. To support the suggested connec-
tion between the specific ERP peak and fMRI-activated area, the correlation of
the same BOLD signal with the other ERP peaks must be lower, if there is any
at all [64]. As a consequence, such analysis cannot prove that any specific peak
of EEG is produced by the neurons located in the fMRI-detected areas alone, but
it definitely shows that they are connected in the specific paradigm.
The search for the covariates between the BOLD signal and widespread neu-
ronal signals, such as the alpha rhythm, remains a more difficult problem due to
the ambiguity of the underlying process, as there are many possible generators of
alpha rhythms corresponding to various functions [136]. As an example, Goldman
et al. [77] and Laufs et al. [79] were looking for the dependency between fMRI
signal and EEG alpha rhythm power during interleaved and simultaneous
EEG/fMRI acquisition, correspondingly. They report similar (negative correlation
in parietal and frontal cortical activity), as well as contradictory (positive correla-
tion) findings, which can be explained by the variations in the experimental setup
[137] or by the heterogeneous coupling between the alpha rhythm and the BOLD
response [79]. Despite the obvious simplification of the correlative methods, they
may still have a role to play in constraining and revealing the definitive forward
model in multimodal applications.
8.4.3.2
Decomposition Techniques
The common drawback of the presented correlative analyses techniques is that
they are based on the selection of the specific feature of the E/MEG signal to be
correlated with the fMRI time trends, which are not so perfectly conditioned to
be characterized primarily by the feature of interest. The variance of the back-
ground processes, which are present in the fMRI data and are possibly explained
by the discarded information from the E/MEG data, can reduce the significance
of the obtained correlation. That is why it was suggested [138] that the entire
E/MEG signal be used, without focusing on its specific frequency band, to derive
the E/MEG and fMRI signal components that have the strongest correlation among
them. The introduction of decomposition techniques (such as basis pursuit, PCA,
ICA, etc.) into the multimodal analysis makes this work particularly interesting.
To perform the decomposition [138], partial least-squares (PLS) regression
was generalized into the tri-PLS2 model, which represents the E/MEG spectrum
as a linear composition of trilinear components. Each component is the product
of spatial (among E/MEG sensors), spectral and temporal factors, in which the
temporal factors have to be maximally correlated with the corresponding temporal
component of the similar fMRI signal decomposition into bilinear components:
products of the spatial and temporal factors. Analysis using tri-PLS2 modeling on
the data from Goldman et al. [77] found a decomposition into three components
corresponding to alpha, theta, and gamma bands of the EEG signal. The fMRI
components found had a strong correlation only in the alpha band component
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