Accounting for the Complex Hierarchical Topology of EEG Phase-Based Functional Connectivity in Network Binarisation

TL;DR

This paper investigates how to effectively binarize EEG functional connectivity networks by considering their hierarchical structure, comparing various methods, and demonstrating the superior performance of the Cluster-Span Threshold in modeling and robustness.

Contribution

It introduces and validates the Cluster-Span Threshold as a novel binarization method that accounts for hierarchical topology in EEG networks, outperforming existing techniques.

Findings

CST performs consistently well in modeling EEG network topology.

CST shows robustness to topological attacks.

Using many edges enhances informational density in EEG connectivity.

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 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.