  In this work we focus on examination and comparison of whole-brain functional
connectivity patterns measured with fMRI across experimental conditions. Direct
examination and comparison of condition-specific matrices is challenging due to
the large number of elements in a connectivity matrix. We present a framework
that uses network analysis to describe condition-specific functional
connectivity. Treating the brain as a complex system in terms of a network, we
extract the most relevant connectivity information by partitioning each network
into clusters representing functionally connected brain regions. Extracted
clusters are used as features for predicting experimental condition in a new
data set. The approach is illustrated on fMRI data examining functional
connectivity patterns during processing of abstract and concrete concepts.
Topological (brain regions) and functional (level of connectivity and
information flow) systematic differences in the ROI-based functional networks
were identified across participants for concrete and abstract concepts. These
differences were sufficient for classification of previously unseen
connectivity matrices as abstract or concrete based on training data derived
from other people.
