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overlap between co-activation graphs (and therefore also low edge overlap), while
redeployment predicts a great deal of node overlap, but little edge overlap. Using
our database of imaging data, we did a co-activation analysis for the eight cognitive
domains having more than 30 experiments: action; attention; emotion; language;
memory; mental imagery; reasoning; and visual perception. The number of exper-
iments (472 total) was not balanced between domains and authors, but otherwise
followed the procedures outlined above. Using Dice's coefficient as our measure
(
d
, where
o
is the number of overlapping elements and
n
is
the total number of elements in each set), we compared the amount of node and
edge overlap between each of the eight domains. As predicted by redeployment, we
found a high degree of node overlap (
d
=
2
(
o
1
,
2
)
/
(
n
1
+
n
2
)
=
0
.
81, SD
=
0
.
04) but very little edge over-
lap (
d
=
0
.
15, SD
=
0
.
04). The difference is significant (two-sample Student's-
t
test,
double-sided
p
001). Figure 2.3 shows a graph of the results. This is just one
among a number of findings that suggest that redeployment is the better supported
approach to understanding the functional topography of the cortex [4-6].
<<
0
.
Fig. 2.3: Mean overlap of nodes vs. edges. A graph of the average Dice's coefficient
for similarity between the sets of nodes and edges in a pair-wise comparison of co-
activation graphs from eight cognitive domains. Difference between the means is
significant
(
p
<<
0
.
001
)
.
Looking at node and edge overlaps is just a simple example of the sorts of com-
parisons one might make using data in this format. Others more specific to graph-
based representations also readily suggest themselves. For instance, one common
form of analysis in graphs is a clique analysis, so called because of its origin in the
analysis of social networks [2]. A clique is a maximal complete sub-graph - that
