Investigating the electrophysiological basis of resting state networks using magnetoencephalography

By Matthew J. Brookes, Mark William Woolrich, Henry Luckhoo, Darren Price, Joanne R. Hale, Mary C. Stephenson, Gareth Robert Barnes, Stephen M. Smith, Peter G. Morris. Proceedings of the National Academy of Sciences of the United States of America, 2011

914 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/1152017969501052ffeec52fd48de79a9d4f66ac

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Olaf Sporns
2. Gustavo Deco
3. Stephen M. Smith
4. Morten L. Kringelbach
5. Vince D. Calhoun
6. Mark William Woolrich
7. Joana Cabral
8. Catie Chang

Citations in the corpus

• MNE software for processing MEG and EEG data (2013, background): “Low-frequency modulations, some of them possibly associated with resting-state networks observed with fMRI, can also be successfully captured with MEG (Brookes et al., 2011; Hipp et al., 2012).”
• How reliable are MEG resting-state connectivity metrics? (2016, methodology): “…functional magnetic resonance imaging (fMRI), both by deconstructing the oscillation-specific functional origin of resting-state networks (Brookes et al., 2011; Marzetti et al., 2013), and as a feasible tool for assessing the short-timescale fluctuations of connectivity at rest (Baker et…”
• Measuring functional connectivity in MEG: A multivariate approach insensitive to linear source leakage (2012, methodology): “A seed location was defined as previously described for these same data (Brookes et al., 2011a) using the motor task and applying Synthetic Aperture Magnetometry (SAM) (Robinson and Vrba, 1998).”
• Spectrally resolved fast transient brain states in electrophysiological data (2016, neutral): “…the delimitation of a particular pattern of activity (or functional network) is not straightforward unless we fix one of these dimensions, by assuming temporal stationarity or by looking at the oscillatory power within a particular frequency band (Baker et al., 2014), (Brookes et al., 2011).”
• Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms (2017, methodology): “Such methods have only recently become available bringing insightful information for the investigation of resting-state FC occurring at faster time-scales (Brookes et al., 2011a, Brookes et al., 2011b, Hipp et al., 2012, Engel et al., 2013, Marzetti et al., 2013, Hipp and Siegel, 2015).”
• Tracking whole-brain connectivity dynamics in the resting state. (2014, neutral): “Large-scale ICNs have also been linked to covarying amplitude fluctuations in alpha and beta neural oscillations between distant regions (Brookes et al. 2011), with emerging work focused on the time-varying aspect of this connectivity (de Pasquale et al. 2011; Baker et al. 2012).”
• Dynamic functional connectivity: Promise, issues, and interpretations (2013, background): “…stronger interactions were driven by alpha and beta band oscillations in the dorsal attention network (de Pasquale et al., 2010) and motor network (Brookes et al., 2011a,b), whereas in the default-mode network, interactions involved theta, alpha and beta oscillations (de Pasquale et al., 2010;…”
• Fast transient networks in spontaneous human brain activity (2014, methodology): “…have revealed that these networks have an electrophysiological basis (Laufs et al., 2003; He et al., 2008; 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…”
• Exploring mechanisms of spontaneous functional connectivity in MEG: How delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillations (2014, neutral): “Notably, these amplitude modulations yield large-scale functional networks that correspond quantitatively to the resting-state networks derived from rs-fMRI (Brookes et al., 2011b).”
• Deep learning for neuroimaging: a validation study (2013, background): “…are highly successful in revealing known brain features with new details [3] (supporting their credibility), in recovering features that differentiate patients and controls [28] (assisting diagnosis and disease understanding), and starting a “resting state” revolution after revealing…”
• Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA (2016, background): “Experiments on real MEG found neuroscientifically interesting networks.”