Interpreting Temporal Fluctuations in Resting-State Functional Connectivity MRI

Abstract

Resting-state functional connectivity is a powerful tool for studying human functional brain networks. Temporal fluctuations in functional connectivity, i.e., dynamic functional connectivity (dFC), are thought to reflect dynamic changes in brain organization and non-stationary switching of discrete brain states. However, recent studies have suggested that dFC might be attributed to sampling variability of static FC. Despite this controversy, a detailed exposition of stationarity and statistical testing of dFC is lacking in the literature. This article seeks an in-depth exploration of these statistical issues at a level appealing to both neuroscientists and statisticians.We first review the statistical notion of stationarity, emphasizing its reliance on ensemble statistics. In contrast, all FC measures depend on sample statistics. An important consequence is that the space of stationary signals is much broader than expected, e.g., encompassing hidden markov models (HMM) widely used to extract discrete brain states. In other words, stationarity does not imply the absence of brain states. We then expound the assumptions underlying the statistical testing of dFC. It turns out that the two popular frameworks -phase randomization (PR) and autoregressive randomization (ARR) -generate stationary, linear, Gaussian null data. Therefore, statistical rejection can be due to nonstationarity, nonlinearity and/or non-Gaussianity. For example, the null hypothesis can be rejected for the stationary HMM due to nonlinearity and non-Gaussianity. Finally, we show that a common form of ARR (bivariate ARR) is susceptible to false positives compared with PR and an adapted version of ARR (multivariate ARR).Application of PR and multivariate ARR to Human Connectome Project data suggests that the stationary, linear, Gaussian null hypothesis cannot be rejected for most participants. However, failure to reject the null hypothesis does not imply that static FC can fully explain dFC. We find that first order AR models explain temporal FC fluctuations significantly better than static FC models. Since first order AR models encode both static FC and one-lag FC, this suggests the presence of dynamical information beyond static FC. Furthermore, even in subjects where the null hypothesis was rejected, AR models explain temporal FC fluctuations significantly better than a popular HMM, suggesting the lack of discrete states (as measured by resting-state fMRI). Overall, our results suggest that AR models are not only useful as a means for generating null data, but may be a powerful tool for exploring the dynamical properties of resting-state fMRI. Finally, we discuss how apparent contradictions in the growing dFC literature might be reconciled.

Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns

“…(g) Dynamic network construction approach. Recent studies have suggested that sliding window correlation analysis with a short window width could introduce artificial fluctuations in estimating DFC (Hindriks et al, 2016;Liegeois, Laumann, Snyder, Zhou, & Yeo, 2017;Lindquist et al, 2014). Thus, in this study, a long window width of 100s was adopted to avoid this issue.…”

Dynamic functional connectivity and individual differences in emotions during social stress

“…Dynamic functional connectivity (dFC) allows for timeresolved analyses of network synchronization by computing functional connectivity over brief segments of time [Hutchison et al, 2013;Zalesky et al, 2014]. Studies employing dFC have shown that synchrony of large-scale brain networks is temporally unstable [Chang and Glover, 2010], although, whether this instability is due to true neural dynamics or some type of physiological or measurement noise, or randomness in signals, is currently debated [Handwerker et al, 2012;Hindriks et al, 2016;Laumann et al, 2016;Nikolau et al, 2016;Liegeois et al, 2017]. Nonetheless, numerous studies have identified a seemingly functional role for dFC in ongoing brain dynamics, either at rest [for a review, see Preti et al, 2017] or during task engagement .…”

Resting-State fMRI Dynamics and Null Models: Perspectives, Sampling Variability, and Simulations

“…Thus, any null model of multivariate timeseries whose characteristics are highly consistent with empirically observed fMRI-based brain measurements has little utility, since the phenomenon that it is testing for is ubiquitous rather than rare. The space of SCC multivariate Gaussians replicates real fMRI network timeseries with sufficient fidelity to induce broad consistency in measurable characteristics between the simulated data and the empirical data it was modeled upon 12 . Moreover, there is no a priori reason to believe that aberrant or "tail" phenomena in this space should be more strongly associated with functionally-relevant brain dynamics than with measurement noise, motion or other artifacts, e.g., the sort of features that might warrant examining a scan for possible removal rather than positioning it as an exemplar of functionally-relevant resting state brain dynamics.…”

“…This example illustrates the difficulties of building hypothesistesting frameworks for phenomena whose distinguishing quantifiable characteristics are not well understood. If, in contrast to 9 , one realizes that the space within which one is working contains the very dynamics that one is trying to rule out (a point subsequently made by 12 ) the conclusions that can be made are unconvincing and uninteresting.…”