Activation detection in event-related fMRI data based on spatio-temporal properties

Template-based activation detection methods, such as cross-correlation, could be difficult to apply in event-related functional MRI data because accurate a priori knowledge about the activation signal patterns is often not available. As a result, several categories of template-free data analysis techniques have been introduced in the fMRI literature. One previously described template-free activation detection technique is based on the feature that activated voxels yield reproducible time course patterns as the subject undergoes the same simulation in repeated epochs. In this paper, spatial information is incorporated as a second feature and a combined univariate measure is formed. The resulting method is shown to offer measurable improvement in detecting activation regions in simulated data in a highly computationally efficient manner. Its practical utility is demonstrated with an experimental data set obtained with a visually guided motor paradigm.