Functional connectivity during the resting state has been shown to change over time (i.e., has a dynamic connectivity). However, resting-state fluctuations, in contrast to task-based experiments, are not initiated by an external stimulus. Consequently, a more complicated method needs to be designed to measure the dynamic connectivity. Previous approaches have been based on assumptions regarding the nature of the underlying dynamic connectivity to compensate for this knowledge gap. The most common assumption is what we refer to as locality assumption. Under a locality assumption, a single connectivity state can be estimated from data that are close in time. This assumption is so natural that it has been either explicitly or implicitly embedded in many current approaches to capture dynamic connectivity. However, an important drawback of methods using this assumption is they are unable to capture dynamic changes in connectivity beyond the embedded rate while, there has been no evidence that the rate of change in brain connectivity matches the rates enforced by this assumption. In this study, we propose an approach that enables us to capture functional connectivity with arbitrary rates of change, varying from very slow to the theoretically maximum possible rate of change, which is only imposed by the sampling rate of the imaging device. This method allows us to observe unique patterns of connectivity that were not observable with previous approaches. As we explain further, these patterns are also significantly correlated to the age and gender of study subjects, which suggests they are also neurobiologically related.
Keywords: dynamic brain connectivity; dynamic covariance; multivariate dynamic covariance; resting-state fMRI; sparse dictionary.
© 2018 Wiley Periodicals, Inc.