The analysis of human brain functional networks is achieved by computing functional connectivity indices reflecting phase coupling and interactions between remote brain regions. In magneto-and electroencephalography, the most often used functional connectivity indices are constructed on Fourier-based cross spectral estimation applied to specific fast and band limited oscillatory regimes. Recently, infraslow arrhythmic fluctuations (below the 1Hz) were recognized as playing a leading role in spontaneous brain activity. The present work aims to propose to assess functional connectivity, from fractal dynamics, thus extending the assessment of functional connectivity to the infraslow arrhythmic or scale-free temporal dynamics of M/EEG-quantified brain activity. Instead of being based on Fourier analysis, new Imaginary Coherence and weighted Phase Lag indices are constructed from complex-wavelet representations. Their performance are, first, assessed on synthetic data, by means of Monte-Carlo simulations, and compared favorably against the classical Fourier-based indices. These new assessment of functional connectivity indices are, second, applied to MEG data collected on 36 individuals, both at rest and during the learning of a visual motion discrimination 1 La Rocca et al. Functional Connectivity assessed from fractal dynamics task. They demonstrate a higher statistical sensitivity, compared to their Fourier counterparts, in capturing significant and relevant functional interactions in the infraslow regime, and modulations from rest to task. Notably, the consistent overall increase in functional connectivity assessed from fractal dynamics from rest to task, correlated with a change in temporal dynamics, as well as with improved performance in task completion, suggests that complex-wavelet weighted Phase Lag index is the sole index able to capture brain plasticity in the infraslow scale-free regime.