  Analysis of data from functional magnetic resonance imaging (fMRI) results in
constructing functional brain networks. Principal component analysis (PCA) and
independent component analysis (ICA) are widely used to generate functional
brain networks. Moreover, dictionary learning and sparse representation provide
some latent patterns that rules brain activities and they can be interpreted as
brain networks. However, these methods lack modeling dependencies of the
discovered networks. In this study an alternative to these conventional methods
is presented in which dependencies of the networks are considered via
correlated sparsity patterns. We formulate this challenge as a new dictionary
learning problem and propose two approaches to solve the problem effectively.
