The number of studies examining functional connectivity of the human brain is increasing rapidly. In this magnetoencephalography (MEG) study, we examined the reliability of connectivity related to language production in a picture naming test-retest paradigm, using data collected from the same participants on 2 separate days. We determined the connections that were reliable (Intraclass Correlation Coefficient, ICC) across both days and also examined the behavioral, functional, and structural properties underlying this reliability. A particularly salient finding among a rich set of results was a reliable pattern of beta connectivity increase in the left motor and frontal regions (0–400 ms and 400–800 ms after picture onset) and gamma connectivity decrease in the bilateral motor regions (800–1200 ms) which we suggest to represent the motor preparation of speech production. Furthermore, the reliable connections tended to be more frequently associated with language performance than the non-reliable ones. Finally, the reliable connections were also linked to stronger functional connectivity, as well as to stronger structural connectivity and shorter structural path length, as determined through diffusion MRI (magnetic resonance imaging). Overall, this study defines reliable language production-related functional connectivity and introduces practices that may increase reliability.

Non-invasive evaluation of functional connectivity, based on source-reconstructed estimates of phase-difference-based metrics, is notoriously non-robust. This is due to a combination of factors, ranging from a misspecification of seed regions to suboptimal baseline assumptions, and residual signal leakage. In this work, we propose a new analysis scheme of source-level phase-difference-based connectivity, which is aimed at optimizing the detection of interacting brain regions. Our approach is based on the combined use of sensor subsampling and dual-source beamformer estimation of all-to-all connectivity on a prespecified dipolar grid. First, a pairwise two-dipole model, to account for reciprocal leakage in the estimation of the localized signals, allows for a usable approximation of the pairwise bias in connectivity due to residual leakage of “third party” noise. Secondly, using sensor array subsampling, the recreation of multiple connectivity maps using different subsets of sensors allows for the identification of consistent spatially localized peaks in the 6-dimensional connectivity maps, indicative of true brain region interactions. These steps are combined with the subtraction of null coherence estimates to obtain the final coherence maps. With extensive simulations, we compared different analysis schemes for their detection rate of connected dipoles, as a function of signal-to-noise ratio, phase difference, and connection strength. We demonstrate superiority of the proposed analysis scheme in comparison to single-dipole models, or an approach that discards the zero phase difference component of the connectivity. We conclude that the proposed pipeline allows for a more robust identification of functional connectivity in experimental data, opening up new possibilities to study brain networks with mechanistically inspired connectivity measures in cognition and in the clinic.