Image Processing Reference
In-Depth Information
In the original BMA framework for E/MEG [151] , i.e., the models
had a flat prior PDF because no additional functional information was available
at that point. Melie-García et al. [152] suggested to use the significance values
of fMRI statistical t-maps to derive
p
(
M
i
) as the mean of all such significance
probabilities across the present in
M
i
compartments. This strategy causes the
models consisting of the compartments with significantly activated voxels to get
higher prior probabilities in BMA. The introduction of fMRI information prior
to BMA analysis reduced the ambiguity of the inverse solution, thus leading to
better localization performance. Although further analysis is necessary to define
the applicability range of the BMA in E/MEG/fMRI fusion, it already looks
promising because of the use of fMRI information as an additional evidence
factor in E/MEG localization, rather than as a hard constraint.
Due to the flexibility of Bayesian formalism, various Bayesian methods for
solving the E/MEG inverse problem already can be easily extended to partially
accommodate evidence obtained from the analysis of fMRI data. For instance,
correlation among different areas obtained from fMRI data analysis can be used
as a prior in the Bayesian reconstruction of correlated sources [153]. The devel-
opment of a neurophysiologic generative model of BOLD signal would allow
many Bayesian inference methods (such as Schmidt et al. [154]) to introduce
complete temporal and spatial fMRI information into the analysis of E/MEG data.
α
i
=∀
1
i
8.5
CONSIDERATIONS AND FUTURE DIRECTIONS
Although the BOLD signal is inherently nonlinear as a function of neuronal acti-
vation, there have been multiple reports of linear dependency between the observed
BOLD response and the selected set of the E/MEG signal features. In general, such
results are not inconsistent with the nonlinearity of BOLD, because, of course, a
nonlinear function can be well approximated in the context of a specific experi-
mental design, regions of interest, or dynamic ranges of the selected features of
E/MEG signals. Besides the LFP/BOLD linearity reported by Logothetis and con-
firmed in the specific frequency bands of an EEG signal during a flashing check-
erboard experiment [155], there have been reports of a strong correlation between
the BOLD signal amplitude and other features of E/MEG responses.
In the past, DC-E/MEG signals have not been given any attention in multi-
modal integration, despite recent experiments showing the strong correlation
between the changes of the observed DC-EEG signal and hemodynamic changes
in the human brain [156]. In fact, such DC-E/MEG/BOLD coupling suggests that
the integration of fMRI and DC-E/MEG might be a particularly useful way to
study the nature of the time variations in the HR signal, which are usually
observed during fMRI experiments but are not explicitly explained by the exper-
imental design or the physics of the MR acquisition process.
Many EMSI methods can be naturally extended to account for fMRI data if
a generative forward model of BOLD signal is available. For instance, direct
universal-approximator inverse methods [157,158] have been found to be very
effective (fast, robust to noise, and to
complex forward models) for the E/MEG
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