Network analysis of the human structural connectome including the brainstem

A computational graph-theoretic analysis of the human structural connectome, incorporating subcortical structures including the brainstem, finds that the brainstem ranks highest across degree, eigenvector, and betweenness centrality metrics. The averaged connectome was derived from 100 healthy adult subjects using Python DIPY and Nibabel libraries. The results indicate the importance of including the brainstem in structural network analyses and suggest that such network-based methods can inform theories of consciousness, including global workspace theory, integrated information theory, and the thalamocortical loop theory.

Study at a glance

Abstract

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.