Network analysis of the human structural connectome including the brainstem

Area of Science:

• Neuroscience
• Computational Biology
• Network Science

Background:

• The anatomical structure of brain networks is crucial for understanding brain function.
• The specific role of the brainstem in structural connectivity remains poorly understood.
• Subcortical structures, including the brainstem, are often underrepresented in connectome studies.

Purpose of the Study:

• To computationally investigate the structural role of the brainstem within the human connectome.
• To analyze the impact of brainstem inclusion on network topology and centrality metrics.
• To identify highly connected regions in the structural connectome.

Main Methods:

• Utilized Python libraries DIPY and Nibabel for structural connectome reconstruction.
• Analyzed data from 100 healthy adult subjects.
• Applied graph-theoretical analyses, computing degree, eigenvector, and betweenness centralities.

Main Results:

• The brainstem emerged as the most highly connected structure across all centrality metrics.
• This finding remained consistent even after normalizing connectivity matrices by volume.
• Brainstem dominance led to decreased network integration and segregation.

Conclusions:

• The brainstem plays a pivotal role in human structural brain networks.
• Including the brainstem is essential for accurate structural connectome analysis.
• Future research should prioritize the brainstem's contribution to network organization.