One common approach to analysing fMRI data is to consider each voxel separately within the framework of the general linear model. The model assumes, at every time point, that the HDR is equal to the scaled and summed version of the events active at that point. A researcher creates a design matrix specifying which events are active at any timepoint. One common way is to create a matrix with one column per overlapping event, and one row per time point, and to mark it if a particular event, say a stimulus, is active at that time point. One then assumes a specific shape for the HDR, leaving only its amplitude changeable in active voxels. The design matrix and this shape are used to generate a prediction of the exact HDR response of the voxel at every timepoint, using the mathematical procedure of convolution. This prediction does not include the scaling required for every event before summing them.