A major advance in morphometry came with the advent of statistical mapping methods that plot features such as gray matter density, cortical thickness in 3D, and can also be used to produce dynamic maps or animations of these features as they change over time. The most commonly applied voxel-based method is voxel-based morphometry, in which MRI scans are split into binary maps of gray and white matter, and spatially aligned across subjects to a common anatomical template. Then a statistic is plotted at each stereotaxic location to show the significance of some effect on anatomy, such as a group difference in gray matter density, or how the rate of change in cortical thickness links with cognition, or with future clinical outcomes. The basic premise of this approach is that the spatial normalization, or warping, of brain scans from multiple subjects can align corresponding anatomical features. Even so, cortical anatomy is so complex and variable across subjects that most standard nonlinear registration algorithms cannot match cortical regions very well, as confirmed by a recent large-scale comparison study of 14 registration methods
