Optimal detection of functional connectivity from high-dimensional EEG synchrony data

Computing phase-locking values between EEG signals is a popular method for quantifying functional connectivity. However, this method involves large-scale, high-resolution datasets, which impose a serious multiple testing problem. Standard multiple testing methods fail to exploit the information from the complex dependence structure that varies across hypotheses in spectral, temporal, and spatial dimensions and result in a severe loss of power. They tend to control the false positives at the cost of hiding true positives. We introduce a new approach, called optimal discovery procedure (ODP) for identifying synchrony that is statistically significant. ODP maximizes the number of true positives for a given number of false positives, and thus offers a theoretical optimum for detecting significant synchrony in a multiple testing situation. We demonstrate the utility of this method with PLV data obtained from a visual search study. We also present simulation analysis to confirm the validity and relevance of using ODP in comparison with the standard FDR method for given configurations of true synchrony. We also compare the effectiveness of ODP with our previously published investigation of hierarchical FDR method (Singh and Phillips, 2010).

Research highlights
► PLVs are used for quantifying EEG synchrony in time, frequency and space.
► High-dimensional PLV datasets impose a serious multiple testing issue.
► Standard multiple testing methods, e.g., FDR fail to detect significant PLVs.
► We introduce a new optimal discovery procedure (ODP).
► Power and validity of ODP is shown using real EEG and simulation analyses.