The thresholding problem and variability in the EEG graph network parameters

Abstract

The graph thresholding is a frequently used practice of eliminating the weak connections in brain functional connectivity graphs. The main aim of the procedure is to delete the spurious connections in the data. However, the choice of the threshold is arbitrary and the effect of the threshold choice in not fully understood. Here we present the description of the changes in the global measures of a functional connectivity graph depending on the different proportional thresholds based on the 146 resting-state EEG recordings. The dynamics is presented in five different synchronization measures (wPLI, ImCoh, Coherence, ciPLV, PPC) in sensors and source spaces. The analysis shows significant changes in the graphs global connectivity measures as a function of the chosen threshold which may influence on the outcome of the study. The choice of the threshold could lead to the different study conclusions, thus it is necessary to improve the reasoning behind the choice of the different analytic options and consider adoption of different analytic approaches.

Task-specific topology of brain networks supporting working memory and inhibition

“…Adjacency matrices were transformed into networks through quartile thresholding (Adamovich et al, 2022; van den Heuvel et al, 2017). We used two different threshold values: 0.5, as previously applied in our research (Zakharov et al, 2020), and 0.8, a more commonly used threshold in the literature.…”