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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    • ", 2013c , 2014 ) . Most studies in humans and animals have examined network dynamics using a sliding window approach ( Chang and Glover , 2010 ; Hutchison et al . , 2013b ; Keilholz et al . "
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    • ", 2013c , 2014 ) . Most studies in humans and animals have examined network dynamics using a sliding window approach ( Chang and Glover , 2010 ; Hutchison et al . , 2013b ; Keilholz et al . "
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