Information Technology Reference
In-Depth Information
is, a set of nodes in a graph that are fully connected with one another, but not fully
connected with any other node in the whole graph. In this context, a clique would
indicate a set of Brodmann areas that are fully co-active with each other, but not
with other areas of the brain; any such neural cliques would obviously be structures
of interest. As in the case of social networks, however, this definition may be too
strict for many purposes. Intuitively, we would be interested in sets of nodes that are
cohesive and relatively isolated - that is, nodes that are highly but not necessarily
fully connected, and much more connected with each other than with other nodes in
the graph. These would represent sets of brain regions that are generally co-active
with each other, but that operate with relative independence from the rest of the
brain. Alba [2] offers the notion of a sociometric clique (an
n
-clique of diameter
n
),
as well as measures of cohesiveness and isolation, that could be adopted here to
discover sets of brain regions with the desired properties. Cohesive, isolated socio-
metric cliques seem likely to correspond to the neural components that cooperate to
support a set of closely related cognitive functions or sub-functions. Whether this is
so is an open scientific question, but such cliques are a far more plausible target for
investigations into the neural components supporting particular cognitive functions
than are individual brain areas. To return us to the issue with which this chapter be-
gan: co-activation graphs allow one to discover (among other things) neural cliques;
in our view, what Anderson et al. should be doing is trying to match ACT-R modules
to these sorts of structures, and not to individual brain areas.
These are far from the only research avenues that these data offer. One can also
look at other features of the graphs, such as local topography, which may help make
plausible inferences about underlying function. For instance, a hub-and-spoke pat-
tern of co-activation may indicate broadcast or information consolidation functions;
in contrast, long strings of connected nodes might indicate serial processing.
We could go on indefinitely, but the point is not to exhaustively list all the possi-
ble analyses one might make with graph-based co-activation data. Instead we would
like to take the opportunity to call to mind the fact that, at very many points in
the history of science, great progress has been made just in virtue of finding the
right format for otherwise well-known data. In a field as young as Cognitive Neu-
roscience it is still more than possible for simple ideas to make a transformative
impact; co-activation graphs may be one of those ideas.
2.5 Relating fMRI to EEG
We would like to conclude by describing one longer term application of co-
activation graphs about which we are especially excited. As the reader is no doubt
aware, a long-standing issue in experimental and clinical neuroscience has been the
question of how to relate data from EEG/MEG to fMRI. Chief among the many ob-
stacles standing in the way of relating the two have been (1) questions over whether
each technology measures the same underlying neural activity [26] and (2) difficulty
in finding the right representational format for the relation, given the vastly differ-
