A Complex Way to Compute fMRI Activation

In functional magnetic resonance imaging, voxel time courses after Fourier or nonFourier "image reconstruction" are complex valued as a result of phase imperfections due to magnetic field inhomogeneities. Nearly all fMRI studies derive functional "activation " based on magnitude-only voxel time courses [2, 6]. Here we propose to directly model the entire complex or bivariate data 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. We investigate the performance of the model on a real dataset, then compare the magnitude-only and complex time course models under varying signal-to-noise ratios in a simulation study with varying activation contrast e#ects.