EEG correlates of time-varying BOLD functional connectivity

By Catie Chang, Zhongming Liu, Michael C. Chen, Xiao Liu, Jeff H. Duyn. NeuroImage, 2013

350 citations recorded by the source corpus.

Recent resting-state fMRI studies have shown that the apparent functional connectivity (FC) between brain regions may undergo changes on time-scales of seconds to minutes, the basis and importance of which are largely unknown. Here, we examine the electrophysiological correlates of within-scan FC variations during a condition of eyes-closed rest. A sliding window analysis of simultaneous EEG-fMRI data was performed to examine whether temporal variations in coupling between three major networks (default mode; DMN, dorsal attention; DAN, and salience network; SN) are associated with temporal variations in mental state, as assessed from the amplitude of alpha and theta oscillations in the EEG. In our dataset, alpha power showed a significant inverse relationship with the strength of connectivity between DMN and DAN. In addition, alpha power covaried with the spatial extent of anticorrelation between DMN and DAN, with higher alpha power associated with larger anticorrelation extent. Results suggest an electrical signature of the time-varying FC between the DAN and DMN, potentially reflecting neural and state-dependent variations.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/158e2af3a944a47c1ce99520101d89e7398e2de8

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In Cognitive Neuroscience

Citations in the corpus

Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, positive): “…ﬂuctuations (Allen et al., 2018; Chang et al., 2013; Damaraju, Hjelm, Plis, & Calhoun, 2017; Tagliazucchi & Laufs, 2014), and some studies suggest that ﬂuctuations in arousal may explain a large proportion of variance in TVFC (Chang et al., 2013; Laumann et al., 2017; Tagliazucchi & Laufs, 2014).”
Fast transient networks in spontaneous human brain activity (2014, background): “…Jann et al., 2010; Liu et al., 2010; Brookes et al., 2011), and are underpinned by much richer spatiotemporal dynamics that may be better characterized using time-varying measures of interactions (Brookes et al., 2014; Chang et al., 2013; Hutchison et al., 2013; de Pasquale et al., 2010, 2012).”
Dynamic functional connectivity of the default mode network tracks daydreaming (2014, background): “However, computational models (Deco and Corbetta, 2011) and empirical data from simultaneous EEG-fMRI (Chang et al., 2013; Tagliazucchi et al., 2012) as well as fMRI coupled with local field potential recordings (Thompson et al., 2013) strongly suggest a neural basis of dynamic FC.”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, background): “…demonstrated that alpha power tended to have an inverse relationship with BOLD FC, the former reporting this relationship with FC between the default-mode and dorsal attention networks (Chang et al., 2013b), and the latter with widespread AAL-atlas-defined region pairs (Tagliazucchi et al., 2012b).”
Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states (2016, methodology): “U N C O R R E C c) Window offset: Typical window offsets used in previous studies range from 1 to 20 s (which covers the range from a single TR step to 50% of the window length) (Chang et al., 2013; Keilholz et al., 2013; Kucyi and Davis, 2014; Shakil et al., 2014).”
Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “These data agree with notions that temporal variability in very low frequency correlations is an emergent, hidden property of higher frequency power spectra (Chang et al., 2013; Tagliazucchi et al., 2012), which is known to change continuously throughout development (Miskovic et al.”
Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? (2016, background): “Several studies have reported correlations between fluctuations in BOLD-fMRI and measures derived from electrophysiological recordings as well as behavioral measures (Tagliazucchi et al., 2012; Di and Biswal, 2013, 2015; Chang et al., 2013; Thompson et al., 2013).”
Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest (2013, background): “Chang et al. (2013b) reported a link between heart rate variability and dynamic FC in some brain regions related to arousal and vigilance, suggesting a neural basis for these fluctuations, and Hutchison et al. (in press) showed that these fluctuations persist in anesthetized monkeys in the absence…”