title: Independent component analysis of fMRI data: examining the assumptions.
abstract: Independent component analysis (ICA), which separates fMRI data into spatially independent patterns of activity, has recently been shown to be a suitable method for exploratory fMRI analysis. The validity of the assumptions of ICA, mainly that the underlying components are spatially independent and add linearly, was explored with a representative fMRI data set by calculating the log-likelihood of observing each voxel's time course conditioned on the ICA model. The probability of observing the time courses from white-matter voxels was higher compared to other observed brain regions. Regions containing blood vessels had the lowest probabilities. The statistical distribution of probabilities over all voxels did not resemble that expected for a small number of independent components mixed with Gaussian noise. These results suggest the ICA model may more accurately represent the data in specific regions of the brain, and that both the activity-dependent sources of blood flow and noise are non-Gaussian.
authors.affiliation: Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, California 92037-1099, USA. martin@salk.edu
authors.forename: M J
authors.initials: MJ
authors.lastname: McKeown
authors.forename: T J
authors.initials: TJ
authors.lastname: Sejnowski
mesh_terms: D003118:Color Perception / Q000502:physiology; D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D016011:Normal Distribution