Spontaneous brain activity and EEG microstates. A novel EEG/fMRI analysis approach to explore resting-state networks

By Francesco Musso, Jürgen Brinkmeyer, Arian Mobascher, Tracy Warbrick, Georg Winterer. NeuroImage, 2010

367 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7329ff3b4ed93ab50e24105c4ab60bfd5048ad99

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In Cognitive Neuroscience

Citations in the corpus

Dynamic functional connectivity: Promise, issues, and interpretations (2013, background): “…fMRI, low-frequency BOLD fluctuations could instead reflect the amplitude (power) modulation of band-limited cortical activity (Britz et al., 2010; Leopold et al., 2003; Mantini et al., 2007; Musso et al., 2010; Nir et al., 2007; Shmuel and Leopold, 2008; for review see Schölvinck et al., 2013).”
Electroencephalographic Resting-State Networks: Source Localization of Microstates (2017, neutral): “Two independent studies on combined EEG-fMRI appeared in the same issue of NeuroImage (Britz et al., 2010; Musso et al., 2010) and were accompanied by two editorial comments (Lehmann, 2010; Laufs, 2010).”
Spatiotemporal dynamics of the brain at rest - Exploring EEG microstates as electrophysiological signatures of BOLD resting state networks (2012, background): “Nevertheless, these studies identified a relatively limited number of RSNs at the group level (Britz et al., 2010; Musso et al., 2010).”
Fast transient networks in spontaneous human brain activity (2014, background): “…there exist distinct functional connectivity states that recur at different points in time is compatible with computational models of neuronal connectivity (Deco et al., 2011) and observations from both fMRI (Allen et al., 2012) and EEG (Britz et al., 2010; Musso et al., 2010; Yuan et al., 2012).”
EEG microstates as a tool for studying the temporal dynamics of whole‐brain neuronal networks: A review (2017, methodology): “Musso and colleagues (Musso et al., 2010) performed k-means cluster analysis of the EEG data for each participant, identifying a fixed number of 10 clusters (microstate maps) per participant with individually different topographies.”
Tracking whole-brain connectivity dynamics in the resting state. (2014, positive): “Indeed, several recent studies have pursued microstate-based fusion of EEG-fMRI resting-state datasets, with promising results suggesting some correspondence between microstates and a limited number of fMR-based ICNs (Britz et al. 2010; Musso et al. 2010; Yuan et al. 2012).”
Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery (2012, positive): “Resulting covariances were then clustered to identify stable states [90]–[92].”
Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques (2013, methodology): “…work, RSN connectivity was found to be dynamic over time and functional connectivity profiles dependent on the temporal scale that was used [Britz et al., 2010; Chang and Glover, 2010; de Pasquale et al., 2010; Honey et al., 2007; Majeed et al., 2009; Musso et al., 2010; Sato et al., 2006].”
Microstates in Resting-State EEG: Current Status and Future Directions (2014, methodology): “Similarly, Musso et al. (2010) identified RSNs using fMRI that were significantly temporally associated with the occurrence of specific microstates, although these authors extracted 10 microstates as opposed to four in the study by Britz et al. (2010) and identified RSN-microstate associations…”