Iterative approach of dual regression with a sparse prior enhances the performance of independent component analysis for group functional magnetic resonance imaging (fMRI) data

NeuroImage

Volumn 63, Issue 4, 2012, Pages 1864-1889

  Kim, Yong Hwan ^a   Kim, Junghoe ^a   Lee, Jong Hwan ^a  

^a Korea University   (South Korea)

Author keywords

Alternating least squares; Back reconstruction; Dual regression; General linear model; Group ICA; Independent component analysis; Iterative dual regression; Non Gaussianity; Sparse prior

Indexed keywords

ALGORITHM; ARTICLE; BOLD SIGNAL; BOOTSTRAPPING; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HEMODYNAMICS; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE QUALITY; INDEPENDENT COMPONENT ANALYSIS; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; NEUROIMAGING; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN MAPPING; COMPUTER SIMULATION; FUNCTIONAL LATERALITY; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; NORMAL DISTRIBUTION; OXYGEN; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; ROC CURVE;

EID: 84866991727     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2012.08.055     Document Type: Article

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