Reliability of graph metrics derived from resting‐state human EEG

Abstract

It is increasingly appreciated that a complete description of brain functioning will necessarily involve the characterization of large-scale interregional temporal synchronization of neuronal assemblies. The need to capture the dynamic formation of such large-scale networks has yielded a renewed interest in the human EEG in combination with a suite of methods for estimating functional connectivity along with the graph theoretical approaches for characterizing network structure. While initial work has established generally good reproducibility for a limited selection of these graph theoretical measures, there remains an obvious need to document the reproducibility of a more extensive array of commonly used graph metrics. We sought to evaluate the test-retest reliability of a much richer suite of graph theoretic measures as applied to weighted networks derived from high-density resting-state human EEG. Our findings were promising overall, with some important qualifications when considering the frequency bands of interest and the method used to calculate functional connectivity as well as some substantial variance between individual graph metrics. In general, the reliability of networks in the α and β frequency bands was improved when functional connectivity was defined solely on the basis of relative phase distributions. In the δ and θ bands, reliability was substantially better when functional connectivity was based on coherence, which incorporates both phase and amplitude information.

Test‐retest reliability of EEG network characteristics in infants

“…Across the study, we report lower reliabilities for delta, beta and gamma frequency bands than for theta, alpha1, and alpha2 frequency bands. This is in concurrence with several previously mentioned studies in which lower beta and gamma reliabilities (Hardmeier et al, 2014;Jin et al, 2011;Kuntzelman & Miskovic, 2017); and lower delta reliabilities (Deuker et al, 2009;Kuntzelman & Miskovic, 2017) were found. Most commonly, the lower reliability of higher frequency bands is explained by the dichotomy between higher and lower frequency bands, where higher frequency bands are more involved in establishing cognitive representation, while lower frequencies are more anatomically constrained (Bassett & Bullmore, 2006).…”

“…Lastly, the higher prevalence of muscle artifacts in the higher frequency bands could limit reliability, especially in children. The small‐worldness index (SWI) is also less reliable in our study, which is in concurrence with previous studies (Hardmeier et al, ; Kuntzelman & Miskovic, ). Since small‐worldness is calculated using both clustering coefficient and path length, and both these characteristics vary independently across sessions, a combination of these variances in the SWI (SWI) could contribute to a lower reliability for the SWI.…”

“…More recently, Kuntzelman & Miskovic tested adults during an eyes‐closed resting state EEG paradigm, comparing global and local graph measures on coherency and dwPLI. They reported good reliability of global dwPLI metrics in theta, alpha1 and alpha2 frequency bands (Kuntzelman & Miskovic, ).…”

“…While these connectivity and graph measures show potential as biomarkers to detect atypical development, biomarkers are only useful if they have a low inter-subject variability and a high test-retest reliability (Hardmeier et al, 2014). Several studies have shown that this is the case for adult EEG/MEG networks (Deuker et al, 2009;Hardmeier et al, 2014;Kuntzelman & Miskovic, 2017).…”

EEG Resting State Functional Connectivity in Adult Dyslexics Using Phase Lag Index and Graph Analysis

“…Relative power was computed as the ratio of the power of the corresponding band and the total power. In the current study, we excluded the gamma band (30–48 Hz) from analysis based on previous evidence suggesting that the gamma frequency range in scalp EEG recordings may be strongly affected by muscle artifact (Whitham et al, 2007), and a recent EEG report indicating low reliability of graph metrics for gamma (Kuntzelman and Miskovic, 2017). …”