Uncovering phase-coupled oscillatory networks in electrophysiological data.

Phase consistent neuronal oscillations are ubiquitous in electrophysiological recordings, and they may reflect networks of phase-coupled neuronal populations oscillating at different frequencies. Because neuronal oscillations may reflect rhythmic modulations of neuronal excitability, phase-coupled oscillatory networks could be the functional building block for routing information through the brain. Current techniques are not suited for directly characterizing such networks. To be able to extract phase-coupled oscillatory networks we developed a new method, which characterizes networks by phase coupling between sites. Importantly, this method respects the fact that neuronal oscillations have energy in a range of frequencies. As a consequence, we characterize these networks by between-site phase relations that vary as a function of frequency, such as those that result from between-site temporal delays. Using human electrocorticographic recordings we show that our method can uncover phase-coupled oscillatory networks that show interesting patterns in their between-site phase relations, such as travelling waves. We validate our method by demonstrating it can accurately recover simulated networks from a realistic noisy environment. By extracting phase-coupled oscillatory networks and investigating patterns in their between-site phase relations we can further elucidate the role of oscillations in neuronal communication.