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ent temporal scale of the two data streams [20]. However, recent research seems to
indicate a mitigation of the first issue; and co-activation graphs may contribute to a
novel approach to the second. We will discuss each of these in turn.
Although there have been for some time, and continue to be, questions about the
neurophysiologial bases of the fMRI signal, converging evidence strongly suggests
that the BOLD signal is best correlated with local field potentials [25, 7, 35]. This
is good news for the project of relating EEG and fMRI, because recent work has
shown that EEG signals can also be analyzed to give estimations of LFP [29, 30].
Although this is hardly to be considered the last word on the subject, it appears
that differences in underlying neurophysiological basis do not necessarily pose an
obstacle to relating the two sources of data.
This brings us to the vast differences in temporal resolution. Since existing fMRI
data cannot be made faster, typical solutions to the mismatch in temporal resolu-
tion have involved lowering the resolution of the EEG signal, by sampling signals
over much longer timescales, and applying mathematical or statistical procedures
(e.g., temporal averaging) to generate a relevant structure such as a local maximum
in the 3D current distribution; this can then be compared to equivalent structures
from fMRI. Vitacco et al. [36] applied this method to relate EEG and fMRI in a
word classification task, but while they were able to obtain agreement between local
maxima for group mean data, there was much poorer correspondence for individ-
ual subjects. One reason for this problem may be that, in averaging or otherwise
manipulating EEG signals, one may be generating artifacts rather than discovering
real features of the data. This is not to say that such attempts at data fusion are not
promising, only that there is room for the introduction and evaluation of alternate
approaches.
We have already outlined our approach to mining large numbers of fMRI studies
and representing the results in graph format. This is relevant to the current issue be-
cause Chaovalitwongse et al. [13] recently developed a way to represent EEG data
that also emphasized cooperative activity and also involved a graph-based repre-
sentation scheme. In the scheme developed by Chaovalitwongse et al., cooperation
between brain areas is measured in terms of the co-variance between EEG elec-
trodes. Although the discovery of temporal correlation in large data sets is far from
a trivial problem. Chaovalitwongse et al. [14, 12] have developed different methods
to make such data mining tractable.
In discussions with Prof. Chaovalitwongse, we quickly realized that combining
our two approaches could help address the issue of relating fMRI and EEG, because
in approaches that focus on the cooperation of brain areas the small-scale temporal
features of the EEG signal are de-emphasized, and the graph-based representational
formats are entirely compatible; given the same underlying spatial segmentation of
the cortex, the two cooperation graphs can be directly overlaid.
Of course, while it is clear that co-activation and co-variation graphs can be easily
overlaid, what is unknown is whether there is any systematic relation between EEG
co-variance and fMRI co-activation. We are currently putting together a research
project to help answer this question (insofar as each graph is providing genuine
information about which brain areas cooperate in supporting various cognitive tasks,
