A COMPLEX DATA METHOD TO COMPUTE FMRI ACTIVATION

In functional magnetic resonance imaging, voxel time courses after Fourier or non-Fourier “image reconstruction ” are com-plex valued as a result of phase imperfections due to mag-netic field inhomogeneities and random noise. Nearly all fMRI studies derive functional “activation ” based on magnitude-only voxel time courses. Here the entire complex or bi-variate data are modeled rather than just the magnitude-only data. A nonlinear multiple regression model is used to model activation of the complex signal, and a likelihood ratio test is derived to determine activation in each voxel. The magnitude-only and complex time course models are applied to a real dataset. 1