The recent discovery of methodological flaws in experimental design and analysis in neuroscience research has raised concerns over the validity of certain techniques used in routine analyses and their corresponding findings. Such concerns have centered around selection bias whereby data is inadvertently manipulated such that the resulting analysis produces falsely increased statistical significance, i.e. type I errors. This has been illustrated recently in flv1RI studies, with excessive flexibility in data collection, and general experimental design issues. Current work from our group has shown how this problem extends to generic voxel-based analysis (and certain technique derivatives such as tract- based spatial statistics) using fractional anisotropy images derived from diffusion tensor imaging. In this work, we demonstrate how this circularity principle can potentially extend to the well-known optimized voxel-based morphometry technique for assessing cortical density differences whereby the principal cause of experimental corruption is due to normalization strategy. Specifically, the popular sum­ of-squared-differences (SSD) metric explicitly optimizes statistical findings potentially inflating type I errors. Additional experimentation demonstrates that this problem is not restricted to the SSD metric but extends to other commonly used metrics such as mutual information, neighborhood cross correlation, and Demons.