Functional magnetic resonance imaging (fMRI) is a non-invasive technique for studying the active human brain. During the fMRI experiment, a sequence of MR images is obtained, where the brain is represented as a set of voxels. The data obtained are a realisation of a complex spatio-temporal process with many sources of variation, both biological and technical. In order to model the activation of interest, it is therefore usually necessary to use highly controlled set of stimuli where the stimuli is repeated several times with resting periods in between. The aim of the analysis is then to find those areas of the brain showing increased or decreased activation during the epochs of stimuli.
With the success of experiments of this type, there is a growing interest within the neuroscience community to extend the experimental paradigm to more complex and more natural stimuli. Examples of the questions asked here is what happens in the brain during rest, meditation, or the viewing of a motion picture? Data of this type is to date usually analyzed using simple correlation analysis or data driven methods such as independent component analysis. Such an analysis will though not reveal the more complicated interaction structure of the activation. This may be investigated using the spatio-temporal point process modelling approach described here. In our model, the activation is modelled as a marked spatio-temporal point process where for each point, the location in space defines the centre of the given activation, the location in time defines the starting time of the activation, and the mark describes the duration and spatial extension.