A0551
Title: Improved activation detection from magnitude and phase functional MRI data
Authors: Dan Adrian - Grand Valley State University (United States) [presenting]
Ranjan Maitra - Iowa State University (United States)
Daniel Rowe - Marquette University (United States)
Abstract: FMRI data consist of both magnitude and phase components (i.e., it is complex-valued), but in the vast majority of statistical analyses, only the magnitude data is utilized and modeled based on a Gaussian approximation. It is shown that using the correct Ricean distribution for the magnitudes, as well as the entire complex-valued data, results in improved activation detection for activation in the magnitude component. Further, as fMRI measures brain activity indirectly through blood flow, the so-called "brain or vein" problem refers to the difficulty in determining whether measured activation corresponds to (desired) brain tissue or (undesired) large veins, which may be draining blood from neighboring regions. Previous work has demonstrated that activation in the phase component "discriminates" between the two: Phase activation occurs in voxels with large, oriented vessels but not in voxels with small, randomly oriented vessels immediately adjacent to brain tissue. Following this motivation, a model is developed that allows for activation in the phase and magnitude components.
