A New Framework for FMRI Data Analysis: Modeling, Image Restoration, and Activation Detection

We propose a new model for event-related functional magnetic resonance imaging (fMRI), and develop a new set of tools for activation detection. A novel feature of our framework is the explicit modeling of the spatial correlation introduced by the scanner. We propose simple, efficient algorithms to estimate model parameters. We develop an activation detection algorithm which consists of two parts: image restoration and least-squares estimation of the parameters of the hemodynamic response function. During the image restoration stage, a total-variation-based approach is employed to restore each data slice, for each time index. The amplitude of the least-squares fit of the hemodynamic response function is then thresholded to yield an estimate of the activation map. We illustrate the promise of our method through several experiments with synthetic data as well as one example with real data.