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

By Catie Chang, Gary H. Glover. NeuroImage, 2009

1,817 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/3c39636a812fc04d3bed7104958fb75b12f33cb4

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In Cognitive Neuroscience

Citations in the corpus

A Comprehensive Assessment of Regional Variation in the Impact of Head Micromovements on Functional Connectomics (2013, neutral): “fluctuations in patterns of functional connectivity (Allen et al., in press; Chang and Glover, 2010; Handwerker et al., 2012; Kang et al., 2011; Smith et al., 2012); most recently, Allen et al.”
Network modelling methods for FMRI (2011, neutral): “, 2010); this was investigated for FMRI data in Chang and Glover (2010). In Sim23, we used 5 nodes, noise of 0.”
Dynamic functional connectivity of the default mode network tracks daydreaming (2014, background): “…demonstrated that FC fluctuates across shorter time-windows (e.g. 30–60 s) during rs-fMRI, with regions that are correlated with one another during some periods being uncorrelated and/or anticorrelated during other periods (Allen et al., 2014; Chang and Glover, 2010; Handwerker et al., 2012).”
Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques (2013, methodology): “Additional work will have to focus on identifying activity profiles, transition states, and task-related changes to the dynamic behavior using novel analysis strategies— particularly at the group level [Chang and Glover, 2010; Deco et al., 2011; Sato et al., 2006].”
On spurious and real fluctuations of dynamic functional connectivity during rest (2015, positive): “…networks during cognitive tasks (Ekman et al., 2012; Lewis et al., 2009; Richiardi et al., 2011, 2013; Shirer et al., 2012), but also at rest (Allen et al., 2014; Chang and Glover, 2010; Hutchison et al., 2013b; Kang et al., 2011; Leonardi et al., 2013; Majeed et al., 2011; Smith et al., 2012).”
Periodic changes in fMRI connectivity (2012, positive): “Frequency based connectivity methods, like time–frequency coherence analysis (Chang and Glover, 2010), might not be sensitive to beat frequency effects or distinct amplitude spectra in the same way as correlation, but it is unclear how much fMRI data is needed to estimate robust and reliable…”
Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity (2012, positive): “…and intracranial recordings) might provide additional information on the existence of meaningful slow sub-bands, corresponding to various processes or connectivity stages, as suggested by early animal studies (Leopold et al., 2003) and more recent resting-state research (Chang and Glover, 2010).”
Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest (2013, methodology): “…confounds of dynamic FC Correlations estimated from short windows have more variance due to the small number of samples but are more sensitive to changes in FC since long windows reduce temporal variability and may miss anticorrelations (Chang and Glover, 2010; Hutchison et al., in press).”
Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery (2012, positive): “As with recent studies exploring resting-state dynamics [85], [86], [88], ICA can be used to capture and exploit the dynamic changes and variability over time in brain activity.”
Eigenvector Centrality Mapping for Analyzing Connectivity Patterns in fMRI Data of the Human Brain (2010, methodology): “It has recently been used for analyzing resting state fMRI data [44].”
NIRS-Based Hyperscanning Reveals Increased Interpersonal Coherence in Superior Frontal Cortex during Cooperation (2011, positive): “For more thorough explanations of continuous wavelet transform (CWT) and WTC as well as illustrative examples, please see Grinsted et al. (2004) and Chang and Glover (2010).”
Evaluating Dynamic Bivariate Correlations in Resting-state fMRI: A comparison study and a new approach (2014, positive): “Following Chang and Glover (2010), a region in the posterior cingulate (3 mm-radius sphere, centered at x = 6, y = 58, and z = 28) was selected as the primary ROI for the default-mode network.”
Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states (2016, methodology): “In various dynamic FC studies based on the SWC, the window length ranges from 8 to 240 s (Chang and Glover, 2010; Kiviniemi et al., 2011; Keilholz et al., 2013; Allen et al., 2014).”
Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “Mounting evidence suggests that the functional connectome exhibits temporal instability that allows it to flexibly switch between multiple different configurations within a scanning session (Allen et al., 2014; Chang and Glover, 2010; Kang et al., 2011; Shen et al., 2015).”
Network attributes for segregation and integration in the human brain. (2013, background): “More recently, empirical studies of resting-state fMRI have documented nonstationarities in functional couplings among remote brain regions [75], and these findings have been confirmed and extended across the human, macaque, and rat brain [76 ,77 ,78] (Figure 4).”
Signature of consciousness in the dynamics of resting-state brain activity (2015, neutral): “Proc Natl Acad Sci USA 102(27):9673–9678.”