Frequency Domain Hybrid Independent Component Analysis of Functional Magnetic Resonance Imaging Data

Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data reveals spatially independent patterns of functional activation. The purely datadriven approach of ICA makes statistical inference difficult. The purpose of this study was to develop a hybrid ICA in the frequency domain that enables statistical inference while preserving advantages of a data-driven ICA. Three normal volunteers were scanned with fMRI while they performed a working memory task. Their data were analyzed with frequency domain hybrid ICA. In each of the subjects, the patterns of activation corresponded to areas expected to be active during the fMRI task. This investigation demonstrates that a hybrid ICA in the frequency domain can statistically map functional activation while preserving the ability of ICA to blindly separate noise sources from the data.