Abstract
The aim of the study was to analyze the relationship between resting state electroencephalographic (EEG) alpha functional connectivity (FC) and small-world organization. For that purpose, Pearson correlation was calculated between FC and small-worldness (SW). Three undirected FC measures were used: magnitude-squared coherence (MSC), imaginary part of coherency (ICOH), and synchronization likelihood (SL). As a result, statistically significant negative correlation occurred between FC and SW for all three FC measures. Small-worldness of MSC and SL were mostly above 1, but lower than 1 for ICOH, suggesting that functional EEG networks did not have small-world properties. Based on the results of the current study, we suggest that decreased alpha small-world organization is compensated with increased connectivity of alpha oscillations in a healthy brain.
Highlights
Functional connectivity (FC) is highly important in physiology at various levels: from molecules to organs and physiological networks are of wide scientific interest, and have high impact in medicine (Ivanov et al, 2016; Lin et al, 2016; Moorman et al, 2016)
Small-worldness calculated from imaginary part of coherency (ICOH) was significantly lower than SW calculated from magnitude-squared coherence (MSC) and synchronization likelihood (SL) for all graph densities analyzed in the current study
For MSC and ICOH, correlations were statistically significant for graph densities 15 ... 50% and for SL 20 ... 50%
Summary
Functional connectivity (FC) is highly important in physiology at various levels: from molecules to organs and physiological networks are of wide scientific interest, and have high impact in medicine (Ivanov et al, 2016; Lin et al, 2016; Moorman et al, 2016). Complex network analysis is based on classical graph theoretical analysis, but focuses on analyzing complex real-life networks (Rubinov and Sporns, 2010). Small-world organization is one of the most frequently analyzed topological properties of functional neural networks. A network is compared to random networks and in order to have small-world properties, the network should be more clustered than a random network, but have similar characteristic path length (Watts and Strogatz, 1998; Albert and Barabási, 2002; Rubinov and Sporns, 2010; Bassett and Bullmore, 2017). Since studying small-world properties of functional brain networks has been widely used
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