A data post-processing protocol for dynamic MRI data to discriminate brain activity from global physiological effects

All fMRI techniques measure stimulus induced focal metabolic and physiological changes in activated brain areas. During the entire fMRI experiment it is necessary to maintain the general physiological condition of the subject as stable as possible. This is not always an easy task. The typical block design in standard fMRI experiments minimizes most of the problems related with these general physiological changes. However in some fMRI experiments, like pharmacological MRI, the experimental setup makes the use of a blocked design impossible. Therefore signal correction algorithms have been developed to correct for these physiological signal instabilities. These algorithms often require elaborate calculation efforts and the data interpretation is often very difficult if no prior knowledge on the nature of the changes exists. In this work we present an algorithm, which has the advantage of being low in calculation effort and the resulting data after correction are easy to interpret. It makes use of a datafit between the general physiological and focal activation related signal changes to eliminate the generalized effects. This algorithm has been tested on simulated and experimentally obtained signal traces suffering both from substantial general signal changes overwhelming the smaller focal activation induced signal changes.