Identification of functional networks in resting state fMRI data using adaptive sparse representation and affinity propagation clustering.

ABSTRACT: Human brain functional system has been viewed as a complex network. To accurately characterize this brain network, it is important to estimate the functional connectivity between separate brain regions (i.e., association matrix). One common approach to evaluating the connectivity is the pairwise Pearson correlation. However, this bivariate method completely ignores the influence of other regions when computing the pairwise association. Another intractable issue existed in many approaches to further analyzing the network structure is the requirement of applying a threshold to the association matrix. To address these issues, we develop a novel scheme to investigate the brain functional networks. Specifically, we first establish a global functional connection network by using the Adaptive Spa

SUBMITTER: Li X 

PROVIDER: S-EPMC4607787 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature