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A Hierarchical Model for Probabilistic Independent Component Analysis of Multi-Subject fMRI Studies
2013
Biometrics
An important goal in fMRI studies is to decompose the observed series of brain images to identify and characterize underlying brain functional networks. Independent component analysis (ICA) has been shown to be a powerful computational tool for this purpose. Classic ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix. Existing
doi:10.1111/biom.12068
pmid:24033125
pmcid:PMC4130464
fatcat:k6cvz7lmvrfhviw2qnr4vkq7ma