Functional Brain Networks Discovery Using Dictionary Learning with Correlated Sparsity
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Functional Magnetic Resonance Imaging (fMRI) helps constructing functional brain networks by using brain activity information. Principal component analysis (PCA) and independent component analysis (ICA) are widely used methods to generate functional brain networks. However, these methods lack modeling dependencies of the constructed methods. In this study as alternative to these conventional methods, we model dependencies of the network via correlated sparsity patterns. We formulate this challenge as a new dictionary learning problem and propose two approaches to solve the problem effectively.
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