Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.neuroimage.2017.09.012
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dc.titleInterpreting temporal fluctuations in resting-state functional connectivity MRI
dc.contributor.authorLiegeois, Raphael
dc.contributor.authorLaumann, Timothy O
dc.contributor.authorSnyder, Abraham Z
dc.contributor.authorZhou, Juan
dc.contributor.authorYeo, BT Thomas
dc.date.accessioned2020-05-05T04:16:00Z
dc.date.available2020-05-05T04:16:00Z
dc.date.issued2017-12-01
dc.identifier.citationLiegeois, Raphael, Laumann, Timothy O, Snyder, Abraham Z, Zhou, Juan, Yeo, BT Thomas (2017-12-01). Interpreting temporal fluctuations in resting-state functional connectivity MRI. NEUROIMAGE 163 : 437-455. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neuroimage.2017.09.012
dc.identifier.issn10538119
dc.identifier.issn10959572
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/167718
dc.description.abstract© 2017 Elsevier Inc. 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 non-stationarity, 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. • Space of stationary models bigger than expected; includes hidden Markov model (HMM).• Phase & autoregressive randomizations test for stationarity, linearity, Gaussianity.• Resting-state fMRI is mostly stationary, linear, and Gaussian.• 1st order autoregressive (AR) model encodes static & one-lag FC.• 1st order AR model explains sliding window correlations very well, better than HMM.
dc.language.isoen
dc.publisherACADEMIC PRESS INC ELSEVIER SCIENCE
dc.sourceElements
dc.subjectScience & Technology
dc.subjectLife Sciences & Biomedicine
dc.subjectNeurosciences
dc.subjectNeuroimaging
dc.subjectRadiology, Nuclear Medicine & Medical Imaging
dc.subjectNeurosciences & Neurology
dc.subjectStationarity
dc.subjectLinear dynamical systems
dc.subjectBrain states
dc.subjectDynamic FC
dc.subjectSurrogate data
dc.subjectAutoregressive model
dc.subjectDEFAULT-MODE NETWORK
dc.subjectLAG STRUCTURE
dc.subjectTIME-SERIES
dc.subjectBRAIN
dc.subjectFMRI
dc.subjectDYNAMICS
dc.subjectORGANIZATION
dc.subjectSTABILITY
dc.subjectPATTERNS
dc.subjectBEHAVIOR
dc.typeArticle
dc.date.updated2020-05-04T16:02:19Z
dc.contributor.departmentDEPT OF ELECTRICAL & COMPUTER ENGG
dc.contributor.departmentDUKE-NUS MEDICAL SCHOOL
dc.contributor.departmentLIFE SCIENCES INSTITUTE
dc.description.doi10.1016/j.neuroimage.2017.09.012
dc.description.sourcetitleNEUROIMAGE
dc.description.volume163
dc.description.page437-455
dc.published.statePublished
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