Comparison of multi-subject ICA methods for analysis of fMRI data

• Published in: Human brain mapping Vol. 32; no. 12; pp. 2075 - 2095
• Main Authors: Erhardt, Erik Barry, Rachakonda, Srinivas, Bedrick, Edward J., Allen, Elena A., Adali, Tülay, Calhoun, Vince D.
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.12.2011; Wiley-Liss; John Wiley & Sons, Inc
• Subjects: Algorithms; back-reconstruction; Biological and medical sciences; brain; Brain mapping; Brain Mapping - methods; Data processing; dual regression; Feeding behavior; fMRI; functional; Functional magnetic resonance imaging; group ICA; Humans; ICA; Image Interpretation, Computer-Assisted - methods; independent component analysis; Investigative techniques, diagnostic techniques (general aspects); Magnetic Resonance Imaging; Medical sciences; Miscellaneous. Technology; Nervous system; Principal Component Analysis; Principal components analysis; Radiodiagnosis. Nmr imagery. Nmr spectrometry; spatio-temporal regression; Human; Reconstruction; Data analysis; Nervous system diseases; functional; Radiodiagnosis; group ICA; ICA; Central nervous system; brain; spatio-temporal regression; Nuclear magnetic resonance imaging; Encephalon; fMRI; dual regression; independent component analysis; Analysis method; back-reconstruction; Comparative study

Spatial independent component analysis (ICA) applied to functional magnetic resonance imaging (fMRI) data identifies functionally connected networks by estimating spatially independent patterns from their linearly mixed fMRI signals. Several multi‐subject ICA approaches estimating subject‐specific t...