Glover, G.H.: Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage 50(1), 81-98

Department of Radiology, Stanford University, Stanford, CA 94305, USA
NeuroImage (Impact Factor: 6.36). 03/2010; 50(1):81-98. 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.

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    • "The application of network science has, however, significantly enhanced the 25 understanding, modelling and characterisation of complex functional brain networks which has gained traction among the cognitive neuro-engineering research communities in the recent past [12] [13]. Advances in neurophysiological recording of brain activity have provided new investigative avenues to support research on acquiring dynamic and non-trivial information relating to patterns of inter- 30 actions between functional brain networks [14] [15] [16]. "
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    ABSTRACT: Recent advances in computational neuroscience have enabled trans-disciplinary researchers to address challenging tasks such as the identification and characterization of cognitive function in the brain. The application of graph theory has contributed to the modelling and understanding the brain dynamics. This paper presents a new approach based on a special graph theoretic concept called minimum connected component (MCC) to detect cognitive load induced changes in functional brain networks using EEG data. The results presented in this paper clearly demonstrate that the MCC based analysis of the functional brain networks derived from multi-channel EEG data is able to detect and quantify changes across the scalp in response to specific cognitive tasks. The MCC, due to its sensitivity to cognitive load, has the potential to be used as a tool not only to measure cognitive activity quantitatively, but also to detect cognitive impairment.
    Neurocomputing 12/2015; 170. DOI:10.1016/j.neucom.2015.03.092 · 2.08 Impact Factor
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    • "Recent studies have shown that resting-state FC exhibits significant variations on a time scale of tens of seconds (Chang and Glover, 2010; Handwerker et al., 2012; Hutchison et al., 2013b; for a review, see Hutchison et al., 2013a). This time-varying or dynamic FC has been characterized with temporal variability of FC estimated at each connection (e.g., Zalesky et al., 2014) or with transition dynamics between a small set of brief FC patterns, known as FC states, believed to be stable for short periods of time and reproducible across time and subjects (Allen et al., 2014). "
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    ABSTRACT: Functional connectivity (FC) measured over extended time periods of resting-state functional magnetic resonance imaging (static FC) has proven useful for characterizing individual differences in human brain function and dysfunction. Recent studies have shown that resting-state FC varies over time scale of tens of seconds (dynamic FC), exhibiting characteristic patterns of temporal variability in FC and transition dynamics of short-lived FC configurations, known as "FC states." However, fundamental properties of FC states, such as their network topology and the dynamics of state transitions, or dynamic FC "flow" between states, as well as their relations to static FC, are relatively unexplored. Here we investigated these basic properties of FC states in humans and assessed how our characterization helps in uncovering individual age-related differences in dynamic FC across the lifespan. We found that dynamic FC was broadly classified into two characteristic FC states with a large proportion of weak FC (flat state) and strong FC exhibiting modular connectivity patterns (modular state), and other states representing their mixtures. These flat and modular FC states were largely constrained by the level of modularity present in static FC. Age-related differences in dynamic FC became evident when we focused on the dynamic flow between the flat and modular FC states in sets of subjects that were expressing lower levels of static FC modularity. These results contribute to our basic understanding of FC dynamics and suggest that classification of FC states can contribute to the detection of individual differences in dynamic brain organization.
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    • "Recently, it has been proposed that such an approach overlooks potentially meaningful fluctuations in the magnitude of functional connections that take place on shorter time scales (Chang and Glover, 2010; Hutchison et al., 2013a; Calhoun et al., 2014; Kopell et al., 2014). A common approach to obtain an shorter timescales (Bassett et al., 2011, 2013). "
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    ABSTRACT: We investigate the relationship of resting-state fMRI functional connectivity estimated over long periods of time with time-varying functional connectivity estimated over shorter time intervals. We show that using Pearson's correlation to estimate functional connectivity implies that the range of fluctuations of functional connections over short time scales is subject to statistical constraints imposed by their connectivity strength over longer scales. We present a method for estimating time-varying functional connectivity that is designed to mitigate this issue and allows us to identify episodes where functional connections are unexpectedly strong or weak. We apply this method to data recorded from $N=80$ participants, and show that the number of unexpectedly strong/weak connections fluctuates over time, and that these variations coincide with intermittent periods of high and low modularity in time-varying functional connectivity. We also find that during periods of relative quiescence regions associated with default mode network tend to join communities with attentional, control, and primary sensory systems. In contrast, during periods where many connections are unexpectedly strong/weak, default mode regions dissociate and form distinct modules. Finally, we go on to show that, while all functional connections can at times manifest stronger (more positively correlated) or weaker (more negatively correlated) than expected, a small number of connections, mostly within the visual and somatomotor networks, do so a disproportional number of times. Our statistical approach allows the detection of functional connections that fluctuate more or less than expected based on their long-time averages and may be of use in future studies characterizing the spatio-temporal patterns of time-varying functional connectivity
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