Source connectivity analysis with MEG and EEG

By Jan-Mathijs Schoffelen, Joachim Gross. Human Brain Mapping, 2009

869 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/98b79ace9103304b749c4d36824a59e0491e736e

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In Cognitive Neuroscience

Citations in the corpus

The WU-Minn Human Connectome Project: An Overview (2013, background): “Linear constrained minimum variance beamformers (LCMV) reconstruct source space data in the time domain and are useful for inferring connectivity in oscillatory brain activity (Brookes et al., 2011; Schoffelen and Gross, 2009).”
Measuring functional connectivity using MEG: Methodology and comparison with fcMRI (2011, positive): “For FC measurement, projection is advantageous (Schoffelen and Gross, 2009) since: 1) It limits the effect of field spread (a single source affecting multiple sensors) making results easier to interpret and allowing spatial comparison between FC maps generated independently using fcMRI and MEG; 2)…”
Global connectivity of prefrontal cortex predicts cognitive control and intelligence (2012, methodology): “In contrast to Langer et al. (2011), we used fMRI to improve spatial localization relative to electroencephalography (Sharon et al., 2007), which is especially important for properly characterizing connectivity (Schoffelen and Gross, 2009).”
Coherence a measure of the brain networks: past and present (2016, neutral): “All electrodes or coils are the sum of the activity from several brain regions so it is hard to resolve the coherence to a specific brain location [42], since the coil or electrode will”
MEG and EEG data analysis with MNE-Python (2013, background): “…coherence and related methods is that they discard or downweight the contributions of the real part of the cross spectrum and, therefore, zero-lag correlations, which can be largely a result of the spatial spread of the measured signal or source estimate distributions (Schoffelen and Gross, 2009).”
Alpha-beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas (2016, neutral): “However, the closer two estimated sources are, the larger the remaining mixing, an effect referred to as ''field spread'' (Schoffelen and Gross, 2009).”
Spectrally resolved fast transient brain states in electrophysiological data (2016, neutral): “This is again a consequence of the ill-posed nature of the source reconstruction inverse problem, which aims to estimate the source activity from the sensor measurements (Schoffelen and Gross, 2009).”
A critical assessment of connectivity measures for EEG data: A simulation study (2013, background): “Only recently, the fact that volume conduction has to be accounted for in EEG (as well as magnetoencephalography, MEG) based brain connectivity studies has been seriously acknowledged (Gómez-Herrero et al., 2008; Haufe, 2011; Haufe et al., 2010b; Nolte and Müller, 2010; Nolte et al., 2004, 2006, 200”
Investigating the electrophysiological basis of resting state networks using magnetoencephalography (2011, methodology): “However, the ill-posed inverse problem (projecting sensor space data into the brain) means that separating real from spurious connectivity in MEG remains difficult (19).”
Frequency-dependent functional connectivity within resting-state networks: An atlas-based MEG beamformer solution (2012, neutral): “A frequent criticism of sensor-level connectivity/network analysis is that the results are biased by the effects of volume conduction/field spread (Schoffelen and Gross, 2009).”