Temporal dynamics of spontaneous MEG activity in brain networks

By F. de Pasquale, S. Della Penna, Abraham Z. Snyder, C. Lewis, Dante Mantini, L. Marzetti, Paolo Belardinelli, L. Ciancetta, V. Pizzella, Gian Luca Romani and 1 more. Proceedings of the National Academy of Sciences of the United States of America, 2010

717 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/15a8f4ce02f833cd35330a6e9d10e24afdb985b7

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In Cognitive Neuroscience

Citations in the corpus

Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms (2017, background): “Novel insights into resting-state FC have been revealed 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…”
A symmetric multivariate leakage correction for MEG connectomes (2015, neutral): “(2014), de Pasquale et al. (2010), Hillebrand et al. (2012), Duncan et al. (2013), Maldjian et al. (2014) and Wens et al. (2014)). In line with Hillebrand et al. (2012), we find an increased number of connections in regions associated with higher source power (occipital, parietal and temporal cortex”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, background): “…in Leopold and Maier, 2012), evidence suggests that ICNs relate to underlying neural activity (Britz et al., 2010; Brookes et al., 2011a,b; de Pasquale et al., 2010, 2012; Fox and Raichle, 2007; He et al., 2008; Laufs, 2008, 2010; Liu et al., 2011; Mantini et al., 2007; Musso et al.,…”
Contributions and challenges for network models in cognitive neuroscience (2014, background): “These findings are consistent with the idea that the topography of RSNs reflects a history of coactivation and common recruitment during taskevoked activity 14 .”
Fast transient networks in spontaneous human brain activity (2014, methodology): “With reference to previous findings (de Pasquale et al., 2010), envelope correlation was computed within 10-s sliding windows between all six ipsilateral ROI pairs and all three contralateral ROI pairs.”
Network attributes for segregation and integration in the human brain. (2013, result): “In parallel to these findings in fMRI data, several studies have reported pronounced nonstationarities in resting-state electromagnetic recordings, for example in measurements of MEG signal power [81] and in synchronization patterns in EEG networks [82,83].”
Time-varying functional network information extracted from brief instances of spontaneous brain activity (2013, background): “Only subsets of these ICs (IC 13, 16, and IC 27, 29, respectively) appeared to resemble the DMN and DAN patterns, respectively.”
Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques (2013, methodology): “The present findings, in addition to the previous work [Chang and Glover, 2010; de Pasquale et al., 2010; Sato et al., 2006], suggest that intranetwork RSN connectivity fluctuations might be another key element that may account for parts of the variability of r 2168 r r 2169 r evoked responses,…”
The Human Connectome Project: A data acquisition perspective (2012, background): “Patterns of MEG resting connectivity can be studied through e.g., correlation of band-limited power time series (de Pasquale et al., 2010) and characterizing node-pair interactions using complex coherency (Marzetti et al., 2008).”
Informatics and Data Mining Tools and Strategies for the Human Connectome Project (2011, background): “…of band-limited power (BLP) will provide measures that reflect the frequency-dependent dynamics of resting and task-evoked brain activity (de Pasquale et al., 2010; Scheeringa et al., 2011). behavIoral, genetIc, and other non-ImagIng measures Measuring behavior in conjunction with mapping…”
Resting-state FMRI confounds and cleanup (2013, neutral): “In humans, no negative correlations between the dorsal attention network and the DMN were observed in MEG signal (de Pasquale et al., 2010).”
Electrophysiological Imaging of Brain Activity and Connectivity – Challenges and Opportunities (2011, background): “Such a strategy has been applied to identify large-scale resting-state rhythms [34], but it is challenged by the low SNR of continuous EEG/MEG signals and is computationally demanding.”