Statistical Significance Assessment of Phase Synchrony in the Presence of Background Couplings: An ECoG Study

Statistical significance testing is a necessary step in connectivity analysis. Several statistical test methods have been employed to assess the significance of functional connectivity, but the performance of these methods has not been thoroughly evaluated. In addition, the effects of the intrinsic brain connectivity and background couplings on performance of statistical test methods in task-based studies have not been investigated yet. The background couplings may exist independent of cognitive state and can be observed on both pre- and post-stimulus time intervals. The background couplings may be falsely detected by a statistical test as task-related connections, which can mislead interpretations of the task-related functional networks. The aim of this study was to investigate the relative performance of four commonly used non-parametric statistical test methods—surrogate, demeaned surrogate, bootstrap resampling, and Monte Carlo permutation methods—in the presence of background couplings and noise, with different signal-to-noise ratios (SNRs). Using simulated electrocorticographic (ECoG) datasets and phase locking value (PLV) as a measure of functional connectivity, we evaluated the performances of the statistical test methods utilizing sensitivity, specificity, accuracy, and receiver operating curve (ROC) analysis. Furthermore, we calculated optimal p values for each statistical test method using the ROC analysis, and found that the optimal p values were increased by decreasing the SNR. We also found that the optimal p value of the bootstrap resampling was greater than that of other methods. Our results from the simulation datasets and a real ECoG dataset, as an illustrative case report, revealed that the bootstrap resampling is the most efficient non-parametric statistical test for identifying the significant PLV of ECoG data, especially in the presence of background couplings.