The aim of this article is to describe a set of regression parameters which can be used within the CerebroMatic toolbox . The general approach of this toolbox is as follows: instead of simple averaging a large number of subject's brain MRI data (usually following tissue segmentation) to generate a reference brain/template, the data is instead analyzed statistically. The main advantage is that this approach is able to take into account the dominating demographic (such as age and gender ) and technical factors (such as field strength and data quality , ). As opposed to the previous application of this idea (and its implementation within the Template-O-Matic toolbox ), the CerebroMatic now uses a much more flexible statistical approach, namely multivariate adaptive regression splines . This allows modeling smooth trajectories of change with much higher flexibility and accuracy, especially in the context of an inhomogeneous group (see , Fig. 1, for an illustration). The result of this modeling is a regression parameter set for each voxel, and each tissue class. From these parameters, a synthetic tissue class can then be generated as the predicted values are linear combinations of the original response values. Hence, the resulting tissue class can be described based on (and thus, matched to) the demographics of a new and independent input population.Fig. 1Overview of the intermediate steps of the image data processing pipeline: each whole brain T1 3D datasets was first bias-corrected (1) and, using CAT12, segmented into GM (2) and WM (not shown). Then, an iterative non-linear registration (3–8) to the respective group mean tissue map was applied, resulting in ever crisper tissue maps (upper row) and corresponding deformation fields (lower row, illustrated here by their Jacobian determinants). This results in conventional DARTEL templates (see Fig. 2, Fig. 4, top rows). The tissue maps were also submitted to the CerebroMatic toolbox, resulting in synthetic DARTEL templates (see Fig. 2, Fig. 4, bottom rows).Fig. 1