Data-Driven Parametric Statistical Testing of Functional Connectivity Between Brain Sources Characterized by Activity with Close-to-Zero Phase Lags
One of the main methodological problems in evaluation of functional connectivity is the spatial leakage (SL) effect which occurs due to volume conduction and leads to false positives in coherence or phase-locking estimates. Several solutions have been already suggested, including the use of the imaginary part of coherency or cross-spectrum. Because these standard metrics are insensitive to zero-phase interactions, they prevent detection of false coupling, resulting from SL, but may underestimate true physiological interactions, characterized by close-to-zero phase lags. Due to the broad neurophysiological evidence, such interactions should not be excluded from consideration. The recently proposed method, referred as Phase Shift Invariant Imaging of Coherent Sources (PSIICOS), became the first implementation of the algorithm which reliably detects interactions for all the range of phase-lags by suppressing the power of SL subspace components of cross-spectrum. However, connectivity values obtained via PSIICOS are non-normalized by construction and depend on source power, so that uncoupled sources with high power profiles may become false positives. This limitation motivated us to develop a statistical test based on randomization of original time series or cross-spectrum in such a way that power distribution in source space is preserved, but phase interactions are eliminated. The generation of covariance matrices from Wishart distribution appeared to be the most reliable method, when applied to data from simulations. Thus, together with the proposed statistical test PSIICOS can be used as an effective instrument applicable to real EEG- or MEG-data in fundamental research or for clinical purposes.