fMRI (functional magnetic resonance imaging) is a powerful non-invasive tool in the study of the function of the brain, used, for example, by psychologists, psychiatrists and neurologists. fMRI can give high quality visualization of the location of activity in the brain resulting from sensory stimulation or cognitive function. It therefore allows the study of how the healthy brain functions, how it is affected by different diseases, how it attempts to recover after damage and how drugs can modulate activity or post-damage recovery. After an fMRI experiment has been designed and carried out, the resulting data must be passed through various analysis steps before the experimenter can get answers to questions about experimentally related activations at the individual or multi-subject level. This paper gives a brief overview of the most commonly used analysis pipeline: data pre-processing, temporal linear modelling and activation thresholding. For more details, see Jezzard et al [1]. fMRI Data In a typical fMRI session a low-resolution functional volume is acquired every few seconds. (MR volumes are often also referred to as ‘‘images’’ or ‘‘scans’’). Over the course of the experiment, 100 volumes or more are typically recorded. In the simplest possible experiment, some images will be taken whilst stimulation (for the remainder of this chapter, reference to ‘‘stimulation’’ should be taken to include also the carrying out of physical or cognitive activity) is applied, and some will be taken with the subject at rest. Because the images are taken using an MR sequence which is sensitive to changes in local blood oxygenation level (BOLD imaging; see Chapters 2 and 3 in Jezzard et al [1]), parts of the images taken during stimulation should show increased intensity, compared with those taken whilst at rest. The parts of these images that show increased intensity should correspond to the brain areas which are activated by the stimulation. The goal of fMRI analysis is to detect, in a robust, sensitive and valid way, those parts of the brain that show increased intensity at the points in time that stimulation was applied. A single volume is made up of individual cuboid elements called voxels (Figure 1). An fMRI data set from a single session can either be thought of as t volumes, one taken every few seconds, or as v voxels, each with an associated time series of t time points. It is important to be able to conceptualize both of these representations, as some analysis steps make more sense when thinking of the data in one way, and others make more sense the other way. An example time-series from a single voxel is shown in Figure 2. Image intensity is shown on the y axis, and time (in scans) on the x axis. As described above, for some of the time points, stimulation was applied, (the higher intensity periods), and at some time points the subject was at rest. As well as the effect of the stimulation being clear, the high frequency noise is also apparent. The aim of fMRI analysis is to identify in which voxels’ time-series the signal of interest is significantly greater than the noise level.