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

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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.

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Data availability

The authors declare that the data supporting the findings of this study are available within the article and its supplementary information files.

Code availability

The code and software used in the current study is proprietary.

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Funding

No funding was received for conducting this study.

Author information

Authors and Affiliations

  1. Omniscient Neurotechnology, Sydney, Australia

    Onur Tanglay, Isabella M. Young, Hugh M. Taylor, Peter J. Nicholas, Stéphane Doyen & Michael E. Sughrue

  2. Centre for Minimally Invasive Neurosurgery, Prince of Wales Hospital, Randwick, NSW, 2031, Australia

    Onur Tanglay & Michael E. Sughrue

  3. Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, 08901, USA

    Nicholas B. Dadario

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  1. Onur Tanglay
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Contributions

OT, SD and MES contributed to the study conception and design. Material preparation, data collection and analysis were performed by HMT, PJN, and OT. The first draft of the manuscript was written by OT and IMY and all authors commented on previous versions of the manuscript. The edited and revised draft of the manuscript was written by ND and OT. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Michael E. Sughrue.

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Conflict of interest

Isabella M. Young, Hugh M. Taylor, Peter J. Nicholas, Stéphane Doyen, and Michael E. Sughrue are employees of and shareholders in Omniscient Neurotechnology. Onur Tanglay is an employee of Omniscient Neurotechnology. Nicholas B. Dadario has no disclosures.

Ethical approval

The data used in the study are from a publicly available dataset. The procedures in the original study were approved by the Institutional Review Boards at UCLA and the Los Angeles Country Department of Mental Health.

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Electronic Supplementary Material

Below is the link to the electronic supplementary material.

11060_2021_3935_MOESM1_ESM.docx

Supplementary file1 Supplementary Results. The PageRank, Eigenvector, EP Difference, and Clustering Coefficient ranks for each parcel across 81 subjects (DOCX 218 kb)

11060_2021_3935_MOESM2_ESM.xlsx

Supplementary file2 Supplementary Methods. A description of the imaging pre-processing pipeline and the creation of the personalized parcellation scheme (XLSX 1389 kb)

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Tanglay, O., Young, I.M., Dadario, N.B. et al. Eigenvector PageRank difference as a measure to reveal topological characteristics of the brain connectome for neurosurgery. J Neurooncol 157, 49–61 (2022). https://doi.org/10.1007/s11060-021-03935-z

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