Abstract
Functional connectivity (FC) and graph measures provide powerful means to analyze complex networks. The current study determines the inter-subject-variability using the coefficient of variation (CoV) and long-term test-retest-reliability (TRT) using the intra-class correlation coefficient (ICC) in 44 healthy subjects with 35 having a follow-up at years 1 and 2. FC was estimated from 256-channel-EEG by the phase-lag-index (PLI) and weighted PLI (wPLI) during an eyes-closed resting state condition. PLI quantifies the asymmetry of the distribution of instantaneous phase differences of two time-series and signifies, whether a consistent non-zero phase lag exists. WPLI extends the PLI by additionally accounting for the magnitude of the phase difference. Signal-space global and regional PLI/wPLI and weighted first-order graph measures, i.e. normalized clustering coefficient (gamma), normalized average path length (lambda), and the small-world-index (SWI) were calculated for theta-, alpha1-, alpha2- and beta-frequency bands. Inter-subject variability of global PLI was low to moderate over frequency bands (0.12<CoV<0.28), higher for wPLI (0.25<CoV<0.55) and very low for gamma, lambda and SWI (CoV<0.048). TRT was good to excellent for global PLI/wPLI (0.68<ICC<0.80), regional PLI/wPLI (0.58<ICC<0.77), and fair to good for graph measures (0.32<ICC<0.73) except wPLI-based lambda in alpha1 (ICC = 0.12). Inter-electrode distance correlated very weakly with inter-electrode PLI (−0.06<rho<0) and weakly with inter-electrode wPLI (−0.22<rho<−0.18). Global PLI/wPLI and topographic connectivity patterns differed between frequency bands, and all individual networks showed a small-world-configuration. PLI/wPLI based network characterization derived from high-resolution EEG has apparently good reliability, which is one important requirement for longitudinal studies exploring the effects of chronic brain diseases over several years.
Highlights
Functional connectivity (FC), graph and nodal network measures are powerful tools to characterize brain function in healthy subjects as well as in neurological and psychiatric diseases [1,2,3]
The current study reports on the inter-subject variability and long-term test-retest-reliability of the PLI and the weighted PLI (wPLI) (PLI/ wPLI) derived from high-resolution eyes-closed resting state EEG and of first-order graph measures in the signal-space
Inter-subject variability of global PLI was low to moderate over frequency bands (0.12,coefficient of variation (CoV),0.28; Table 1) and very low for PLI based gamma, lambda and SWI (CoV,0.022, CI 95%: 0.01– 0.027)
Summary
Functional connectivity (FC), graph and nodal network measures are powerful tools to characterize brain function in healthy subjects as well as in neurological and psychiatric diseases [1,2,3]. Scalp signals of EEG are an admix of source activity, volume conduction, i.e. the spatial spread of the electric field during its way from its source through the cerebro-spinal fluid and skull [9], and the influence of the reference electrode [10]. These latter two properties may artificially induce FC as the same signal is measured at different electrodes [11]. The FC estimation by the PLI is based on a consistent lag between the instantaneous phases of two electrodes and is less sensitive to zero-lag phaserelations typical for common sources. The weighted PLI (wPLI) is an extension to the PLI and is reported to be less sensitive to noise [12]
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