For both types of study designs, the BOLD signal at an “activated” voxel is expected to change in a way related to the transitions from one condition to another. This rationale allows us to make inference about regionally specific effects in response to the task/stimulus. For experiments with only two conditions, voxel-wise statistical inference may be simply based on a Student's t-test or period cross correlation [5]. A more general and flexible approach is based on the general linear model (GLM) [110, 111]. Stimulus functions encoding the occurrence of a particular event or experimental state (e.g. boxcar-functions) are convolved with a hemodynamic response function (HRF) to form regressors in the GLM. Fitting the GLM to the data allows for the estimation of model parameters and the statistic inference against a null hypothesis (i.e. the voxel is not activated). Such inference is classic in terms of statistics, as opposed to more recent methods based on the Bayesian inference which provides the posterior probability that the voxel is activated given the data [112]. All of these methods discussed so far are model-driven
