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randomly selected from the remaining domains. This set of experiments, equally
balanced between the domains, will be the sample for that year's co-activation
analysis.
On this balanced sample we will run at least the following kinds of analysis.
(1) For each domain, and for the entire set, we will generate a co-activation graph,
constructed using the method outlined above, using Brodmann areas as nodes, and
including only activations with a range (see above) of less than 5 mm. The calculated
chance of activation and co-activation, as well as the binomial probability and
2
value will be reported for each pair of Brodmann areas, allowing researchers to
set their own probability thresholds. (2) For each of the co-activation graphs, we
will do a clique analysis (see below). Lancaster et al. [23] review some methods
for generating cliques from brain activation data, and there are many other well-
established methods for extracting cliques of various descriptions from graphs [1,
8, 9, 16]. Finally, (3) for all of the co-activation graphs and cliques, we will project
them onto the adjacency graph (shown above) and calculate the average minimum
graph distance (the “scatter” in the cortex) of the included nodes. All of this data
will be made available for download from the lab web site, at
Before moving on to the next section, where we describe some of the uses to
which these data have been put, and how it can be applied in the future, it is worth
saying a word about our reliance on Brodmann areas as the basis for the analy-
ses. It is of course legitimate to wonder whether the sub-division of the cortex
into Brodmann areas will be a feature of our final functional map of the human
brain; one rather suspects it will be fully superseded by some yet-to-be developed
topographical scheme. Yet Brodmann areas remain the lingua franca in Cognitive
Neuroscience for reporting findings, and sticking to this tradition will make results
using these analyses easier to relate to past findings. Moreover, for the purposes
we have described here - investigating the functional cooperation between brain
areas involved in supporting different functions - virtually any consistent spatial
division of the brain will do, and regions the size of Brodmann areas offer ade-
quate spatial resolution for the required analysis. For, while the spatial resolution
of a single fMRI image is on the order of 3 mm or better, there are questions both
about the accuracy and precision of repeated fMRI, both within and between par-
ticipants, effectively reducing its functional resolution [28]. It is arguable, then, that
the use of Brodmann-sized regions of the cortex for representing the contribution
of individual brain areas to cognitive tasks is consistent with the realistic (con-
servatively estimated) spatial resolution of current imaging technologies [10, 34].
In any case, it should be noted that the coordinates of each activation are also
recorded in the database; if a Brodmann-based spatial scheme does not appear to
produce useful or legitimate results, other spatial divisions of the cortex can cer-
tainly be substituted, and the very same sort of analysis performed. For instance,
one can use the ALE (activation likelihood estimates) paradigm [33] to extract prob-
able activations for arbitrarily defined neural volumes and build graphs from these
data [23].
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