Imaging brain dynamics using independent component analysis (2001, methodology): “ICA, applied to fMRI data, has proven to be a powerful method for detecting task-related activations, including unanticipated activations [52]–[54], [59]–[61] that could not be detected by standard hypothesis-driven analyses.”
A method for making group inferences from functional MRI data using independent component analysis (2001, unknown): “…component analysis as applied to fMRI data can be used to separate either spatially [McKe-own et al., 1998a] or temporally [Biswal and Ulmer, 1999] independent sources and works well in both situations when appropriate assumptions are met [Calhoun et al., 2001b; McKeown and Sejnowski, 1998].”
Independent component analysis of nondeterministic fMRI signal sources (2003, methodology): “…signal sources based on their non-Gaussian distributions could thus be used in differentiating nondeterministic physiological signal sources from fMRI data (Kiviniemi et al., 2000; Eke and Hermán, 1999; West et al., 1999; Obrig et al., 2000; McKeown et al., 1998; McKeown and Sejnowski, 1998).”
Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data (2010, background): “…into account non-linear effects and that is based upon the assumption that the complexities of brain function can be modeled simply, with no more than a few dozen ICs that are assumed to reflect perfectly temporally synchronized brain activity (McKeown and Sejnowski, 1998; Thomas et al., 2002).”
Support vector machine learning-based fMRI data group analysis (2007, methodology): “Treating each functional image as an independent observation without considering the acquisition time, the SVM-based ap-
proaches have the disadvantage of ignoring the dynamic changes in the brain response, which can be explored using other multivariate methods such as ICA, CVA, or PLS.”