Time-frequency dynamics of resting-state brain connectivity measured with fMRI

ArticleinNeuroImage 50(1):81-98 · March 2010with58 Reads
DOI: 10.1016/j.neuroimage.2009.12.011
Most studies of resting-state functional connectivity using fMRI employ methods that assume temporal stationarity, such as correlation and data-driven decompositions computed across the duration of the scan. However, evidence from both task-based fMRI studies and animal electrophysiology suggests that functional connectivity may exhibit dynamic changes within time scales of seconds to minutes. In the present study, we investigated the dynamic behavior of resting-state connectivity across the course of a single scan, performing a time–frequency coherence analysis based on the wavelet transform. We focused on the connectivity of the posterior cingulate cortex (PCC), a primary node of the default-mode network, examining its relationship with both the “anticorrelated” (“task-positive”) network as well as other nodes of the default-mode network. It was observed that coherence and phase between the PCC and the anticorrelated network was variable in time and frequency, and statistical testing based on Monte Carlo simulations revealed the presence of significant scale-dependent temporal variability. In addition, a sliding-window correlation procedure identified other regions across the brain that exhibited variable connectivity with the PCC across the scan, which included areas previously implicated in attention and salience processing. Although it is unclear whether the observed coherence and phase variability can be attributed to residual noise or modulation of cognitive state, the present results illustrate that resting-state functional connectivity is not static, and it may therefore prove valuable to consider measures of variability, in addition to average quantities, when characterizing resting-state networks.
    • "Many studies show temporal test-retest reliability and reproducibility of functional resting-state networks [117][118][119]. It has been argued that neural coupling configurations are dynamic and transient over time with fluctuations in connection strengths [120, 121]. As arousal, sleep [122], conscious [123], cognitive [124] and emotional [125] states constitute influencing factors, only replication in independent, longitudinal samples can confirm the stability of the reported findings. "
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    • "The electric signals with their inherent high-frequency content have been shown to change its connectivity patterns at subsecond time scales [44, 45]. However, even slowly varying resting-state fMRI signals acquired at rest are not stationary in time and their connectivity profile may change over time [46]. The most straightforward and simplest way to analyze restingstate fMRI connectivity is by extracting the signal intensity time series at a small seed region of interest (ROI) that is a priori selected by the researcher. "
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    • "Nonstationarity in the context of electrophysiology is frequently evaluated using spectral descriptors (Wong et al. 2006; Halliday et al. 2009 ). Prior methods for evaluating nonstationarity in RSFC have relied on bivariate analyses, that is, analyzing multivariate data taken pairwise (Chang and Glover 2010; Hindriks et al. 2016). However, in these methods the number of free parameters grows in proportion to the square of the dimensionality of the data set, which limits computational tractability when the number of regions is large. "
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