Predicting the principal components of cortical morphological variables

The generating mechanism for the gyrification of the mammalian cerebral cortex remains a central open question in neuroscience. Although many models have been proposed over the years, very few were able to provide empirically testable predictions. In this paper, we assume a model in which the cortex folds for all species of mammals according to a simple mechanism of effective free energy minimization of a growing self-avoiding surface subjected to inhomogeneous bulk stresses to derive a new set of morphological variables to express cortical morphology. In terms of these new variables, we seek to understand the variance present in two morphometric datasets: a human MRI harmonized multi-site dataset comprised by 3324 healthy controls (CTL) from 4 to 96 years old and a collection of different mammalian cortices with morphological measurements extracted manually. This is done using a standard Principal Component Analysis (PCA) of the cortical morphometric space. We prove there is a remarkable coincidence (typically less than 8°) between the resulting principal components vectors in each datasets and the directions corresponding to the new variables. This shows that the new, theoretically-derived variables are a set of natural and independent morphometrics with which to express cortical shape and size.