PT - JOURNAL ARTICLE
AU - Liégeois, Raphaël
AU - Laumann, Timothy O.
AU - Snyder, Abraham Z.
AU - Zhou, Helen J.
AU - Thomas Yeo, B.T.
TI - Interpreting Temporal Fluctuations in Resting-State Functional Connectivity MRI
AID - 10.1101/135681
DP - 2017 Jan 01
TA - bioRxiv
PG - 135681
4099 - http://biorxiv.org/content/early/2017/05/09/135681.short
4100 - http://biorxiv.org/content/early/2017/05/09/135681.full
AB - 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, which contrasts with the fact that all FC measures depend on sample statistics. One implication is that stationarity does not imply the absence of brain states. We then expound the assumptions underlying two frameworks - phase randomization (PR) and autoregressive randomization (ARR) - widely used to generate null data for statistical testing of dFC. It turns out that both PR and ARR rely on assumptions of stationarity, linearity and Gaussianity. Therefore statistical rejection does not necessarily imply non-stationarity, but can also be due to nonlinearity or non-Gaussianity. We further 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. After all, AR models are dynamical FC models in the sense that they encode linear dynamic interactions beyond static FC. We find that AR models could explain temporal FC fluctuations significantly better than static FC models. We also find that AR models explain temporal FC fluctuations significantly better than a popular model assuming discrete brain states, 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 functional connectivity. We also discuss how apparent contradictions in the growing dFC literature might be reconciled.