Surfin’ endocasts: the good and the bad on brain form

Abstract

Digital anatomy and computed morphometrics currently represent basic tools in anthropology, zoology, and paleontology. Despite the user-friendly interfaces of the programs, these methods require a robust expertise in statistics, biomedical imaging, and computer graphics. Geometrical modeling is aimed at normalizing shape variation as to compare forms within a shared reference space. As any other modeling approach, it can be used to test hypotheses or to investigate the structure of sample variation. In both cases, models refer to specific variables and parameters, and they follow numerical criteria that are based on algebraic and conventional rules. If models are interpreted too broadly and confused with the real anatomical elements, conclusions can be seriously biased. This risk can be particularly relevant when dealing with morphometric methods that do not use anatomical references, like sliding landmarks, surface analysis, or voxel-based morphometry. All these techniques are largely employed in craniology, paleoneurology, and evolutionary neuroanatomy. Following these approaches, elements are analyzed as “objects” and not as “anatomical elements,” introducing noise and drawbacks due to the registration processes and to the absence of constraints associated with anatomical boundaries. Downsides can be avoided by interpreting geometric models as specific representations of a set of properties of the original anatomical systems and not as generalized effigy of biological elements.