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.
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.
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Functional Magnetic Resonance Imaging
Analysis of data from functional magnetic resonance imaging
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helps
results in
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by using brain activity information
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methods
A
Moreover, dictionary learning and sparse representation provide some latent patterns that rules brain activities and they can be interpreted as brain networks.
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constructed methods
discovered networks
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as
an
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, we model
is presented in which
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network
networks are considered