Abstract
In order to continuously respond to a changing environment and support self-generating cognition and behaviour, neural communication must be highly flexible and dynamic at the same time than hierarchically organized. While whole-brain fMRI measures have revealed robust yet changing patterns of statistical dependencies between regions, it is not clear whether these statistical patterns —referred to as functional connectivity— can reflect dynamic large-scale communication in a way that is relevant to cognition. For functional connectivity to reflect actual communication, we propose three necessary conditions: it must span sufficient temporal complexity to support the needs of cognition while still being highly organized so that the system behaves reliably; it must be able to adapt to the current behavioural context; and it must exhibit fluctuations at sufficiently short timescales. In this paper, we introduce principal components of connectivity analysis (PCCA), an approach based on running principal component analysis on multiple runs of a time-varying functional connectivity model to show that functional connectivity follows low- yet multi-dimensional trajectories that can be reliably measured, and that these trajectories meet the aforementioned criteria to index flexible communication between neural populations and support moment-to-moment cognition.
Competing Interest Statement
The authors have declared no competing interest.