Detecting cortical activities from fMRI time-course data using the MUSIC algorithm with forward and backward covariance averaging.

A method is proposed for processing time-course fMRI data taken with successive single-shot echo-planar imaging. The proposed method uses a two-dimensional version of the multiple signal classification (MUSIC) algorithm and the technique called covariance averaging, both of which were developed in the field of sensor-array processing. The proposed method consists of four steps: calculate the averaged data covariance matrix, determine the number of activities using this covariance matrix, estimate the locations of the activities, and estimate their time evolution curves. Computer simulation results showed that a nearly fourfold improvement in the spatial resolution can be attained due to the method's super-resolution capability.