Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques

By R. Matthew Hutchison, Thilo Womelsdorf, J. Gati, Stefan Everling, Ravi S. Menon. Human Brain Mapping, 2013

749 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/360464d7fca9b61cd0ffdd957196a87f1c3ce702

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In Cognitive Neuroscience

Citations in the corpus

The human connectome: Origins and challenges (2013, background): “…models, and is now rapidly gathering momentum with a growing number of empirical demonstrations of fluctuations in functional connectivity in fMRI (Chang and Glover, 2010; Hutchison et al., 2012; Jones et al., 2012; Smith et al., 2012) as well as EEG/MEG recordings (de Pasquale et al., 2010).”
Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms (2017, background): “…by MEG studies (de Pasquale et al., 2010, Brookes et al., 2011b, Hipp et al., 2012, Hipp and Siegel, 2015) and by investigations on the temporal dynamics of FC (Chang and Glover, 2010, Handwerker et al., 2012, Smith et al., 2012, Hutchison et al., 2013a, Allen et al., 2014, Zalesky et al., 2014).”
Contributions and challenges for network models in cognitive neuroscience (2014, background): “Nonstationarity in functional couplings has since been found in empirical data 83 and is becoming a new focus of investigation in restingstate fMRI.”
Time-resolved resting-state brain networks (2014, methodology): “Connectivity was estimated using pairwise linear correlation in regionally averaged rsfMRI time series data falling within fixed-length time windows (19, 21, 22, 26).”
Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia (2014, background): “…variability (Fox andRaichle, 2007), reports have alsohighlighted the pitfall of over-interpreting fluctuations in FC as having a neural origin, when in fact these fluctuations can arise from basic BOLD signal properties (Handwerker et al., 2012; Hutchison et al., 2013; Keilholz et al., 2013).”
Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? (2016, methodology): “…correlation coefficient, which is the most widely used FC measure in resting-state fMRI research (Sakoglu et al., 2010; Chang and Glover, 2010; Hutchison et al., 2013b; Handwerker et al., 2012; Keilholz et al., 2013; Tagliazucchi et al., 2012; Jones et al., 2012; Thompson et al., 2013;…”
The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance (2015, neutral): “Finally, although we directly compared the Multiplication of Temporal Derivatives (MTD) approach to sliding-window Pearson’s correlation, the standard approach used to calculate time-resolved connectivity, there are many techniques used to estimate these measures (Hutchison et al., 2013), and as suc”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, neutral): “…relevance of BOLD TVFC comes from studies that have shown that TVFC is present during unconsciousness due to general anesthesia (Barttfeld et al., 2015; Hutchison, Womelsdorf, Gati, et al., 2013; Liang, Liu, & Zhang, 2015), when one should expect no changes in cognitive state or level of arousal.”
Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states (2016, methodology): “Greater variability was observed when shorter windows were used, in accordance with previous studies (Hutchison et al., 2013a, 2013b; Keilholz et al., 2013).”
EEG correlates of time-varying BOLD functional connectivity (2013, neutral): “Similar fluctuations in correlation patterns have been observed across other networks in humans with fMRI (Kiviniemi et al., 2011; Rack-Gomer and Liu, 2012; Sakoglu et al., 2010) and MEG (de Pasquale et al., 2010), and in the BOLD signal of anesthetized monkeys (Hutchison et al., 2012).”
Time-varying functional network information extracted from brief instances of spontaneous brain activity (2013, methodology): “Recent approaches toward this goal have relied mainly on examining temporal variability in the fMRI signal correlations (or coherences) using the time–frequency analysis or sliding window method (22, 24, 25, 27).”