2 research outputs found
Network analysis of the human structural connectome including the brainstem: a new perspective on consciousness
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
Network analysis of the human structural connectome including the brainstem
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