Biomedical Engineering Reference
In-Depth Information
approximates a standard normal distribution. A joint confidence interval with at least
95% coverage probability for
g
(
f
,
t
)
−
g
(
f
,
T
)
is:
m
95
ˆσ
g
(
g
(
f
,
t
)
−
g
(
f
,
T
)
±
f
,
t
)+
ˆσ
g
(
f
,
T
)
(4.38)
where
m
95
is the 97.5% quantile of the distribution
MAX
(
t
n
,
T
n
)=
max
t
∈{
t
1
,...,
t
n
},
T
∈{
T
1
,...,
T
n
},
f
∈{
f
1
,...,
f
m
}
|
N
t
,
T
,
f
|
(4.39)
where
N
t
,
T
,
f
are independent
N
random variables. This is equivalent to apply-
ing the Bonferonni correction for
t
n
T
n
f
m
tests to control the family-wise error rate
(FWER).
The utility of the ERC approach was demonstrated through its application to hu-
man electrocorticographic recordings (ECoG) of a simple language task [Korze-
niewska et al., 2008]. ERC analyses of these ECoG recordings revealed frequency-
dependent interactions, particularly in high gamma (
(
0
,
1
)
60 Hz) frequencies, between
brain regions known to participate in the recorded language task, and the temporal
evolution of these interactions was consistent with the putative processing stages of
this task.
>
4.1.8 Multimodal integration of EEG and fMRI signals
In recent years there has been a growing awareness in scientific society that for a
better understanding of information processing in the brain there is a need to inte-
grate the modalities offering different spatial and temporal resolution. EEG/ERP pro-
vide high temporal resolution, but much poorer spatial resolution; in contrast fMRI
(functional magnetic resonance imaging) offers high spatial resolution, but poor time
resolution. The blood oxygenation response (BOLD) measured by means of fMRI
depends on the oxygen uptake which is connected with neuronal activity. However
in contrast to EEG, BOLD response is an indirect and delayed metabolic correlate
of neuronal process. The challenges to scientists are: to better understand the link
between BOLD and neural activity and to capitalize on complementary properties of
EEG and BOLD in the investigation of brain processes. To this aim multimodal anal-
yses are undertaken based on concurrent measurement of both modalities. In [Bli-
nowska et al., 2009] the connection between neuronal activity and BOLD response is
delineated and four approaches to the analysis of simultaneously acquired EEG/ERP-
fMRI are described, namely: i) fMRI informed EEG for constrained source localiza-
tion, ii) EEG or ERP-informed fMRI analysis, iii) parallel independent component
analysis (ICA), and iv) joint ICA (jICA) application for matching temporal sources
from the EEG with spatial sources from fMRI.
fMRI informed EEG uses hemodynamic priors for cortical activity localization. In
[Urbano et al., 1998] and [Babiloni et al., 2000], a statistically significant percentage
increase of the BOLD signal during the task compared to the rest state was used
to define the priors in the solution of the linear inverse problem. The information
was derived from the BOLD responses of the relevant cortical areas, taking into
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