Detecting stimulus driven changes in functional brain connectivity

Abstract

We consider the problem of detecting stimulus driven changes in brain functional connectivity. Estimating functional connectivity from fMRI data sampled over a small time period is difficult - there is simply not enough data to permit reliable estimates. We investigate the use of a sparse Gaussian graphical model regularized by a graph learned from data sampled over a longer time period. We establish a framework to identify the changes in brain connectivity driven by short-term stimuli. Results of experiments on both synthetic and real fMRI data illustrate the attributes of our methods as well as the difficulty of the problem.