Title
Bayesian Models for Complex-Valued MRI
Speaker
Raquel Prado, University of California, Santa Cruz
Abstract
Detecting which voxels/regions are actived by an external stimulus is one of the main goals in the analysis of functional magnetic resonance imaging (fMRI) data. Voxel time series in fMRI are complex-valued signals consisting of magnitude and phase components, however, most studies discard the phase and only use the magnitude data. We present a Bayesian variable selection approach for detecting activation at the voxel level from complex-valued fMRI (f(c)MRI) recorded during task experiments. We show that this approach leads to fast and improved detection of activation when compared to alternative magnitude-only approaches. We discuss and illustrate modeling extensions that incorporate additional spatial structure via kernel convolution for more flexible analysis of f(c)MRI. The complex-valued spatial model encourages voxels to be activated in clusters, which is appropriate in applied settings, as the execution of complex cognitive tasks, and therefore brain activation, usually involve populations of neurons spanning across many voxels rather than isolated voxels. Finally, we present models that can handle multi-subject data and allow us to infer connectivity at the region-specific level in addition to voxel-specific activation. Model performance is evaluated through extensive and physically realistic simulation studies and in the analysis of human f(c)MRI.
Note: Seminars are free and open to the public. Reception to follow.
