A variety of voxel-based morphometric analysis methods have been adopted by the neuroimaging community in the recent years. In this commentary we describe why voxel-based statistics, which are commonly used to construct statistical parametric maps, are very limited in characterizing morphological differences between groups, and why the effectiveness of voxel-based statistics is significantly biased toward group differences that are highly localized in space and of linear nature, whereas it is significantly reduced in cases with group differences of similar or even higher magnitude, when these differences are spatially complex and subtle. The complex and often subtle and nonlinear ways in which various factors, such as age, sex, genotype and disease, can affect brain morphology, suggest that alternative, unbiased methods based on statistical learning theory might be able to better quantify brain changes that are due to a variety of factors, especially when relationships between brain networks, rather than individual structures, and disease are examined.