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.