Scale-free dynamics, quantified as power law spectra from magnetoencepholagraphic (MEG) recordings of Human brain activity, may play an important role in cognition and behavior. To date, their characterization remain limited to uni-variate analysis. Independently, functional connectivity analysis usually entails uncovering interactions between remote brain regions. In MEG, specific indices (e.g., Imaginary coherence ICOH and weighted Phase Lag Index wPLI) were developed to quantify phase synchronization between time series reflecting activities of distant brain regions and applied to oscillatory regimes (e.g., α-band in (8, 12) Hz). No such indices has yet been developed for scale-free brain dynamics. Here, we propose to design new indices (w-ICOH and w-wPLI) based on complex wavelet analysis, dedicated to assess functional connectivity in the scale-free regime. Using synthetic multivariate scale-free data, we illustrate the potential and efficiency of these new indices to assess phase coupling in the scale-free dynamics range. From MEG data (36 individuals), we demonstrate that w-wPLI constitutes a highly sensitive index to capture significant and meaningful group-level changes of phase couplings in the scale-free (0.1, 1.5) Hz regime between rest and task conditions.