Reliability of graph metrics derived from resting-state human EEG

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 a and b frequency bands was improved when functional connectivity was defined solely on the basis of relative phase distributions. In the d and h bands, reliability was substantially better when functional connectivity was based on coherence, which incorporates both phase and amplitude information.