Independent component analysis of fMRI data in the complex domain

In BOLD fMRI a series of MR images is acquired and examined for task-related amplitude changes. These functional changes are small, so it is important to maximize detection efficiency. Virtually all fMRI processing strategies utilize magnitude information and ignore the phase, resulting in an unnecessary loss of efficiency. As the optimum way to model the phase information is not clear, a flexible modeling technique is useful. To analyze complex data sets, independent component analysis (ICA), a data-driven approach, is proposed. In ICA, the data are modeled as spatially independent components multiplied by their respective time-courses. There are thus three possible approaches: 1) the time-courses can be complex-valued, 2) the images can be complex-valued, or 3) both the time-courses and the images can be complex-valued. These analytic approaches are applied to data from a visual stimulation paradigm, and results from three complex analysis models are presented and compared with magnitude-only results. Using the criterion of the number of contiguous activated voxels at a given threshold, an average of 12-23% more voxels are detected by complex-valued ICA estimation at a threshold of /Z/ > 2.5. Additionally, preliminary results from the complex models reveal a phase modulation similar to the magnitude time-course in some voxels, and oppositely modulated in other voxels.