Can fMRI functional connectivity index dynamic neural communication and cognition?

To support self-generating cognition and behaviour, neural communication must be highly flexible and dynamic, but also structured. While whole-brain fMRI measures have revealed robust yet changing patterns of statistical dependencies between regions, it is unclear whether these statistical patterns -referred to as functional connectivity (FC)- can reflect dynamic large-scale communication in a way that is relevant to human cognition; or just reflect, for example, homeostatic processes. For FC to reflect cognition, and therefore actual communication, we propose that at least three conditions must be met: it must span sufficient temporal complexity to support cognition's demands while being highly organized so that the system behaves reliably; it must be able to adjust to behavioural circumstances; and it must exhibit fluctuations at timescales compatible with cognition' timescales. We trained multiple models of time-varying FC on fMRI data from the Human Connectome Project across three behavioural conditions: at rest, during a working memory task, and a motor task; and characterised them using Principal Component Analysis. We show that FC follows low- yet multi-dimensional trajectories that can be reliably measured, and that these trajectories can satisfy the aforementioned requirements. Although these are necessary but not sufficient conditions, it remains possible that time-varying FC may potentially index key aspects of neural communication underlying cognitive function.