Eigenvector PageRank difference as a measure to reveal topological characteristics of the brain connectome for neurosurgery

Abstract

Purpose: Applying graph theory to the human brain has the potential to help prognosticate the impacts of intracerebral surgery. Eigenvector (EC) and PageRank (PR) centrality are two related, but uniquely different measures of nodal centrality which may be utilized together to reveal varying neuroanatomical characteristics of the brain connectome.

Methods: We obtained diffusion neuroimaging data from a healthy cohort (UCLA consortium for neuropsychiatric phenomics) and applied a personalized parcellation scheme to them. We ranked parcels based on weighted EC and PR, and then calculated the difference (EP difference) and correlation between the two metrics. We also compared the difference between the two metrics to the clustering coefficient.

Results: While EC and PR were consistent for top and bottom ranking parcels, they differed for mid-ranking parcels. Parcels with a high EC centrality but low PR tended to be in the medial temporal and temporooccipital regions, whereas PR conferred greater importance to multi-modal association areas in the frontal, parietal and insular cortices. The EP difference showed a weak correlation with clustering coefficient, though there was significant individual variation.

Conclusions: The relationship between PageRank and eigenvector centrality can identify distinct topological characteristics of the brain connectome such as the presence of unimodal or multimodal association cortices. These results highlight how different graph theory metrics can be used alone or in combination to reveal unique neuroanatomical features for further clinical study.

Keywords: Centrality; Connectivity; Graph theory; Neurosurgery.