Mapping the human brain has been a topic of interest for the last few
decades. In spite of its incredible complexity it is now possible to
map the brain using a combination of advanced data representation and
data processing algorithms supported on the huge computational power
that is available nowadays. In this work we describe an approach for
mapping whole-brain functional connectivity. The starting point of our
work is a set of high resolution functional magnetic resonance images
(fMRI) obtained with a 7T magnetic field that cover a wider brain
volume than usual. The fMRIs are then used to build the so called
brain functional connectivity network. These networks extracted from
the brain can be represented as graphs, i.e., a set of nodes (regions)
and a set of edges connecting such nodes. With the networks
represented as graphs we apply network mining techniques to them,
namely clustering and modularity algorithms that allow us, for
instance, to identify functional modules of the brain. Presumably, the
increased resolution will allow to obtain more detailed information
and potential to uncover additional structure. Due to the size of the
graphs all the algorithms must be optimized in order to minimize the
used resources.