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

Functional Brain Networks Discovery Using Dictionary Learning with Correlated Sparsity