A New Method For Fmri Activation Detection

The objective of fMRI data analysis is to detect the region of the brain that gets activated in response to a specific stimulus presented to the subject. We develop a new algorithm for activation detection in event-related fMRI data. We utilize a forward model for fMRI data acquisition which explicitly incorporates physiological noise, scanner noise and the spatial blurring introduced by the scanner. After slice-by-slice image restoration procedure that independently restores each data slice corresponding to each time index, we estimate the parameters of the hemodynamic response function (HRF) model for each pixel of the restored data. In order to enforce spatial regularity in our estimates, we model the prior distribution of the HRF parameters as a generalized Gaussian Markov random field (GGMRF) model. We develop an algorithm to compute the maximum a posteriori (MAP) estimates of the parameters. We then threshold the amplitude parameters to obtain the final activation map. We illustrate our algorithm by comparing it with the widely used general linear model (GLM) method. In synthetic data experiments, under the same probability of false alarm, the probability of correct detection for our method is up to 15% higher than GLM. In real data experiments, through anatomical analysis and benchmark testing using block paradigm results, we demonstrate that our algorithm produces fewer false alarms than GLM.