Questions and controversies in the study of time-varying functional connectivity in resting fMRI

Abstract

The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as "dynamic" or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.

Keywords: Brain dynamics; Brain networks; Functional connectivity; Rest; Review; fMRI.

Figure 1.

Growth of the fMRI TVFC literature. The field of TVFC research has grown rapidly, as demonstrated by the increasing number of fMRI TVFC papers published each year (as indexed by PubMed). To account for overall growth in the rate of scientific publishing, the height of the bars has been normalized by the total number of all papers published in each year. Because of inconsistencies in the way TVFC analyses are described, these figures likely represent a conservative estimate of the size of the fMRI TVFC literature, particularly for earlier years. For details on the search terms used to identify TVFC papers, please see the Supporting Information.

Figure 2.

Schematic illustration of common analysis and modeling approaches for studying TVFC in fMRI data. Green arrows indicate a typical workflow based on sliding-window correlation, which is currently the most common data-driven approach for estimating TVFC. Blue arrows represent the diversity of alternative data-driven approaches. Some alternative approaches (e.g., HMMs) estimate functional connectivity states directly from BOLD time series, while others (e.g., phase synchrony, a time-frequency method) are more similar to the sliding-window approach. Regardless of how FC time series or functional connectivity states are estimated, it is possible to calculate a wide range of measures describing their properties. For example, fluctuations in the strength of FC between two areas can be tested for associations with concurrently measured behavioral variables, while network measures can be used to describe the properties of whole-brain FC patterns and how they change over time. Whether TVFC estimates are considered to constitute bona fide “dynamics” depends on the specific feature of interest and null model against which they are tested. Orange arrows represent a computational modeling workflow that fits a dynamic biophysical model to empirical BOLD time series in order to estimate model parameters and simulate underlying fast timescale neural activity.