Sex Differences in the Human Connectome

The human brain and the neuronal networks comprising it are of immense interest to the scientific community. In this work, we focus on the structural connectivity of human brains, investigating sex differences across male and female connectomes (brain-graphs) for the knowledge discovery problem "Which brain regions exert differences in connectivity across the two sexes?". One of our main findings discloses the statistical difference at the pars orbitalis of the connectome between sexes, which has been shown to function in language production. Moreover, we use these discriminative regions for the related learning problem "Can we classify a given human connectome to belong to one of the sexes just by analyzing its connectivity structure?". We show that we can learn decision tree as well as support vector machine classification models for this task. We show that our models achieve up to 79% prediction accuracy with only a handful of brain regions as discriminating factors. Importantly, our results are consistent across two data sets, collected at two different centers, with two different scanning sequences, and two different age groups (children and elderly). This is highly suggestive that we have discovered scientifically meaningful sex differences.