Large-scale cortical correlation structure of spontaneous oscillatory activity

By Joerg F. Hipp, D. Hawellek, Maurizio Corbetta, Markus Siegel, Andreas K. Engel. Nature Neuroscience, 2012

1,094 citations recorded by the source corpus.

Little is known about the brain-wide correlation of electrophysiological signals. We found that spontaneous oscillatory neuronal activity exhibited frequency-specific spatial correlation structure in the human brain. We developed an analysis approach that discounts spurious correlation of signal power caused by the limited spatial resolution of electrophysiological measures. We applied this approach to source estimates of spontaneous neuronal activity reconstructed from magnetoencephalography. Overall, correlation of power across cortical regions was strongest in the alpha to beta frequency range (8–32 Hz) and correlation patterns depended on the underlying oscillation frequency. Global hubs resided in the medial temporal lobe in the theta frequency range (4–6 Hz), in lateral parietal areas in the alpha to beta frequency range (8–23 Hz) and in sensorimotor areas for higher frequencies (32–45 Hz). Our data suggest that interactions in various large-scale cortical networks may be reflected in frequency-specific power envelope correlations.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/0d823fd2d187ff5fca225661cc7baf08369f8e05

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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).”
Spectrally resolved fast transient brain states in electrophysiological data (2016, positive): “Alternatively, we can orthogonalise the time courses prior to estimating envelope correlation (Brookes et al., 2011), (Hipp et al., 2012), (Colclough et al., 2015).”
Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms (2017, background): “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).”
Fast transient networks in spontaneous human brain activity (2014, background): “Graph theoretical analyses of MEG band-limited power have revealed strong hubs in dorsal prefrontal cortex, lateral parietal cortex, and temporal cortex (in essence those areas represented by state 1), but notably not the PCC (Hipp et al., 2012).”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, background): “, 2011a,b), whereas in the default-mode network, interactions involved theta, alpha and beta oscillations (de Pasquale et al., 2010; Hipp et al., 2012).”
Graph analysis of functional brain networks: practical issues in translational neuroscience (2014, background): “In the latter cases, referring to “graphs”, which do not make explicit assumptions on the nature of the links but rather emphasize the aspect of mathematical modeling, seems to be more appropriate and can be used to improve the interdisciplinary exchange.”
HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity (2013, background): “The component of Y(t,f) parallel to X, Y ║ X (t,f), corresponds to the part of Y(t,f) that can be instantaneously and linearly predicted from X, and “ shares with it the co-variability in power due to measuring the same sources in two different sites ” (Hipp et al. 2012).”
Exploring mechanisms of spontaneous functional connectivity in MEG: How delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillations (2014, positive): “…simultaneous fMRI and EEG (de Munck et al., 2007; Difrancesco et al., 2008; Goldman et al., 2002; Laufs et al., 2003b; Mantini et al., 2007; Ritter et al., 2009) and, more recently MEG (Brookes et al., 2011b; de Pasquale et al., 2010; Hipp et al., 2012; Liu et al., 2010; Nikouline et al., 2001).”
Moving magnetoencephalography towards real-world applications with a wearable system (2018, positive): “For example, it is well known that efficient communication between spatially separate cortical regions is key to healthy brain function, and that neural oscillations help mediate such connectivity28,29.”