A true spatiotemporal approach for activation detection in functional MRI

A goal in functional Magnetic Resonance Imaging (fMRI) data analysis is determining whether a certain region of brain is activated by presented temporal stimuli. Since the fMRI data is a sequence of images, spatiotemporal models are needed and spatially and temporally correlated noise plays a crucial role in the models. Until very recently, most attention has focussed on temporally correlated but spatially independent models. And spatial correlation has been dealt with in an ad hoc fashion. We develop, for the first time, a properly formulated true spatiotemporal detection statistic based on a spatially and temporally correlated noise model. Additionally, we develop a theoretical performance analysis method for comparing different test statistics through Asymptotic Relative Efficiency (ARE) for the first time in fMRI. We perform simulations for the comparison of new test statistic with a standard statistic as well.