The underlying anatomical structure is fundamental to the study of brain
networks and is likely to play a key role in the generation of conscious
experience. We conduct a computational and graph-theoretical study of the human
structural connectome incorporating a variety of subcortical structures
including the brainstem, which is typically not considered in similar studies.
Our computational scheme involves the use of Python DIPY and Nibabel libraries
to develop an averaged structural connectome comprised of 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. Our results highlight the importance of
including the brainstem in structural network analyses. We suggest that
structural network-based methods can inform theories of consciousness, such as
global workspace theory (GWT), integrated information theory (IIT), and the
thalamocortical loop theory.Comment: 23 pages, 5 figure