Evidence for Transient, Uncoupled Power and Functional Connectivity Dynamics

There is growing interest in studying the temporal structure in brain network activity, in particular, dynamic functional connectivity (FC), which has been linked in several studies with cognition, demographics and disease states. The sliding window approach is one of the most common approaches to compute dynamic FC. However, it cannot detect cognitively relevant and transient temporal changes at time scales of fast cognition, that is, on the order of 100 ms, which can be identified with model-based methods such as the HMM (Hidden Markov Model) and DyNeMo (Dynamic Network Modes) using electrophysiology. These new methods provide time-varying estimates of the 'power' (i.e., variance) and of the functional connectivity of the brain activity, under the assumption that they share the same dynamics. But there is no principled basis for this assumption. Using a new method that allows for the possibility that power and FC networks have different dynamics (Multi-dynamic DyNeMo) on resting-state magnetoencephalography (MEG) data, we show that the dynamics of the power and the FC networks are not coupled. Using a (visual) task MEG dataset, we show that the power and FC network dynamics are modulated by the task, such that the coupling in their dynamics changes significantly during the task. This work reveals novel insights into evoked network responses and ongoing activity that previous methods fail to capture, challenging the assumption that power and FC share the same dynamics.