BRAIN ACTIVITY DETECTION Statistical Analysis of fMRI Data

We are concerned with modelling and analysing fMRI data. An fMRI experiment is a series of images obtained over time under two different conditions, in which regions of activity are detected by observing differences in blood magnetism due to hemodynamic response. In this paper we propose a spatiotemporal model for detecting brain activity in fMRI. The model makes no assumptions about the shape or form of activated areas, except that they emit higher signals in response to a stimulus than non-activated areas do, and that they form connected regions. The Bayesian spatial prior distributions provide a framework for detecting active regions much as a neurologist might; based on posterior evidence over a wide range of spatial scales, simultaneously considering the level of the voxel magnitudes together with the size of the activated area. A single spatiotemporal Bayesian model allows more information to be obtained about the corresponding magnetic resonance study. Despite higher computational cost, a spatiotemporal model improves the inference ability since it takes into account the uncertainty in both the spatial and the temporal dimension. A simulated study is used to test the model applicability and sensitivity.