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 the time scales of fast cognition, i.e. on the order 100 milliseconds, which can be identified with model-based methods such as 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. In this work, we propose Multi-dynamic Network Modes (M-DyNeMo), an extension to DyNeMo, that allows for the possibility that the power and the FC networks have different dynamics. Using this new method on magnetoencephalography (MEG) data, we show intriguingly that the dynamics of the power and the FC networks are uncoupled. Using a (visual) task MEG dataset, we also show that the power and FC network dynamics are modulated by the task, such that the coupling in their dynamics changes significantly during task. This new method 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. HighlightsO_LIWe show that our proposed model - Multi-dynamic Network Modes (M-DyNeMo) - infers transient, uncoupled dynamics for time-varying variance (i.e. power) and functional connectivity (FC) in MEG data. C_LIO_LIM-DyNeMo infers plausible power and FC networks that are reproducible across different subjects, datasets, and parcellations. C_LIO_LIM-DyNeMo reveals task modulates the dynamics of the power and the FC networks, which induces a coupling between dynamics. C_LIO_LIThe evoked FC network response to a (visual) task is different to the evoked network response in power. C_LIO_LIThe datasets and scripts for performing all the analysis in this paper are made publicly available. C_LI