Latest revision as of 22:49, 22 March 2021

Abstract

Structural brain networks may be reconstructed from diffusion MRI tractography data and have great potential to further our understanding of the topological organisation of brain structure in health and disease. Network reconstruction is complex and involves a series of processesing methods including anatomical parcellation, registration, fiber orientation estimation and whole-brain fiber tractography. Methodological choices at each stage can affect the anatomical accuracy and graph theoretical properties of the reconstructed networks, meaning applying different combinations in a network reconstruction pipeline may produce substantially different networks. Furthermore, the choice of which connections are considered important is unclear. In this study, we assessed the similarity between structural networks obtained using two independent state-of-the-art reconstruction pipelines. We aimed to quantify network similarity and identify the core connections emerging most robustly in both pipelines. Similarity of network connections was compared between pipelines employing different atlases by merging parcels to a common and equivalent node scale. We found a high agreement between the networks across a range of fiber density thresholds. In addition, we identified a robust core of highly connected regions coinciding with a peak in similarity across network density thresholds, and replicated these results with atlases at different node scales. The binary network properties of these core connections were similar between pipelines but showed some differences in atlases across node scales. This study demonstrates the utility of applying multiple structural network reconstrution pipelines to diffusion data in order to identify the most important connections for further study.

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• http://eprints.gla.ac.uk/208068/1/208068.pdf under the license http://creativecommons.org/licenses/by/4.0

• http://europepmc.org/articles/PMC4214749 under the license http://creativecommons.org/licenses/by/4.0

• https://dx.doi.org/10.1371/journal.pone.0111262

• https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0111262&type=printable

• http://dx.plos.org/10.1371/journal.pone.0111262,

http://dx.doi.org/10.1371/journal.pone.0111262 under the license cc-by

• http://europepmc.org/articles/PMC4214749,

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4214749,

https://ui.adsabs.harvard.edu/abs/2014PLoSO...9k1262P/abstract,

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0111262,

http://eprints.gla.ac.uk/208068,

https://paperity.org/p/60453848/consensus-between-pipelines-in-structural-brain-networks,

http://discovery.ucl.ac.uk/1469321,

http://discovery.ucl.ac.uk/1469321/5/Clayden.1469321_journal.pone.0111262.pdf,

https://core.ac.uk/display/29416145,

https://academic.microsoft.com/#/detail/1975363200 under the license http://creativecommons.org/licenses/by/4.0/

• http://europepmc.org/articles/PMC4214749?pdf=render,

https://doaj.org/toc/1932-6203

• https://discovery.ucl.ac.uk/id/eprint/1469321,

https://discovery.ucl.ac.uk/id/eprint/1469321/5/Clayden.1469321_journal.pone.0111262.pdf