  The underlying anatomical structure is fundamental to the study of brain
networks, but the role of brainstem from a structural perspective is not very
well understood. We conduct a computational and graph-theoretical study of the
human structural connectome incorporating a variety of subcortical structures
including the brainstem. Our computational scheme involves the use of Python
DIPY and Nibabel libraries to develop structural connectomes using 100 healthy
adult subjects. We then compute degree, eigenvector, and betweenness
centralities to identify several highly connected structures and find that the
brainstem ranks highest across all examined metrics, a result that holds even
when the connectivity matrix is normalized by volume. We also investigated some
global topological features in the connectomes, such as the balance of
integration and segregation, and found that the domination of the brainstem
generally causes networks to become less integrated and segregated. Our results
highlight the importance of including the brainstem in structural network
analyses.
