Accounting for the complex hierarchical topology of EEG phase-based functional connectivity in network binarisation

Smith, Keith, Abasolo, Daniel Emilio and Escudero, Javier (2017) Accounting for the complex hierarchical topology of EEG phase-based functional connectivity in network binarisation PLoS ONE, 12 (10), e0186164.

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Abstract

Research into binary network analysis of brain function faces a methodological challenge in selecting an appropriate threshold to binarise edge weights. For EEG phase-based functional connectivity, we test the hypothesis that such binarisation should take into account the complex hierarchical structure found in functional connectivity. We explore the density range suitable for such structure and provide a comparison of state-of-the-art binarisation techniques, the recently proposed Cluster-Span Threshold (CST), minimum spanning trees, efficiency-cost optimisation and union of shortest path graphs, with arbitrary proportional thresholds and weighted networks. We test these techniques on weighted complex hierarchy models by contrasting model realisations with small parametric differences. We also test the robustness of these techniques to random and targeted topological attacks.We find that the CST performs consistenty well in state-of-the-art modelling of EEG network topology, robustness to topological PLOS 1/31 network attacks, and in three real datasets, agreeing with our hypothesis of hierarchical complexity. This provides interesting new evidence into the relevance of considering a large number of edges in EEG functional connectivity research to provide informational density in the topology.

Item Type:	Article
Divisions :	Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences
Authors :	Name Email ORCID Smith, Keith Abasolo, Daniel Emilio D.Abasolo@surrey.ac.uk Escudero, Javier
Date :	20 October 2017
DOI :	10.1371/journal.pone.0186164
Copyright Disclaimer :	© 2017 Smith et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Depositing User :	Clive Harris
Date Deposited :	29 Sep 2017 12:33
Last Modified :	16 Jan 2019 18:57
URI:	http://epubs.surrey.ac.uk/id/eprint/842436

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