Representative scale-invariant characteristics of male and female brains in magnetic resonance images

This paper investigates the link between sex and the human brain from anatomical MRI data, where a primary confound is the size difference between male and female groups. Anatomy is characterized by the 3D scale-invariant feature transform (SIFT), where features are salient image regions that are automatically identified and normalized according local size or scale. Experiments use T1-w MRI data of 422 healthy unrelated subjects from the Human Connectome Project (HCP) dataset (211 males, 211 females, 22-36 years of age). We found that brain sex may be predicted via image-to-image feature matching with 91.9% accuracy, that classification is driven largely by weakly-informative features distributed throughout the brain and shared by unique subsets of subjects, and that a pair of features identified in the right and left thalamic regions of 97% of subjects predicts sex with 74% accuracy. Misclassified subjects exhibit features typical of the opposite sex in one or both hemispheres.