  Detecting and evaluating regions of brain under various circumstances is one
of the most interesting topics in computational neuroscience. However, the
majority of the studies on detecting communities of a functional connectivity
network of the brain is done on networks obtained from coherency attributes,
and not from correlation. This lack of studies, in part, is due to the fact
that many common methods for clustering graphs require the nodes of the network
to be `positively' linked together, a property that is guaranteed by a
coherency matrix, by definition. However, correlation matrices reveal more
information regarding how each pair of nodes are linked together. In this
study, for the first time we simultaneously examine four inherently different
network clustering methods (spectral, heuristic, and optimization methods)
applied to the functional connectivity networks of the CA1 region of the
hippocampus of an anaesthetized rat during pre-ictal and post-ictal states. The
networks are obtained from correlation matrices, and its results are compared
with the ones obtained by applying the same methods to coherency matrices. The
correlation matrices show a much finer community structure compared to the
coherency matrices. Furthermore, we examine the potential smoothing effect of
choosing various window sizes for computing the correlation/coherency matrices.
