Independent component analysis applied to fMRI data: a generative model for validating results
Vince D. Calhoun, Tülay Adalı, Godfrey D. Pearlson · 2002
We introduce and apply a synthesis/analysis model for analyzing functional Magnetic Resonance Imaging (fMRI) data using independent component analysis (ICA). Our model assumes statistically independent spatial sources in the brain. We also assume that the fMRI scanner acquires overdetermined data such that there are more time points than brain sources. We discuss the properties of each of the signals present in the model. The analysis portion of the model includes several candidates for spatial smoothing, ICA algorithm, and data reduction. We use the Kullback-Leibler divergence between the estimated source distributions and the "true" distributions as a measure of the optimality of the final ICA decomposition. Using this model, we generate fMRI-like data and optimize the analysis stage as a function of ICA algorithm, data reduction scheme, and spatial smoothing.