Identifying true brain interaction from EEG data using the imaginary part of coherency.

By Guido Nolte, O. Bai, L. Wheaton, Z. Mari, Sherry Vorbach, Mark Hallett. Clinical Neurophysiology, 2004

1,752 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d4477f03d3da6c48d32e1456270b076af19b85ae

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In Cognitive Neuroscience

Citations in the corpus

Introduction to machine learning for brain imaging (2011, background): “, 2007), (d) measures for determining synchrony, coherence or causal relations in data (e.g. Meinecke et al., 2005; Brunner et al., 2006; Nolte et al., 2004;Marzetti et al., 2008; Nolte et al., 2006, 2008; Gentili et al., 2009) and (e) algorithms for assessing and counteracting non-stationarity in d”
Anelastic Earth structure from the coherency of the ambient seismic field (2008, methodology): “In this paper we do not use the arithmetic mean, since the coherency does not have a Gaussian distribution [Thomson and Chave, 1991; Nolte et al., 2004].”
A symmetric multivariate leakage correction for MEG connectomes (2015, positive): “…seek to be robust to source leakage artefacts either assess metrics of phase lag between sources, which are insensitive to linear leakage effects Nolte et al. (2004), Stam et al. (2007), Baccala and Sameshima (2001), Kaminski and Blinowska (2014), or attempt to correct for leakage before…”
How reliable are MEG resting-state connectivity metrics? (2016, background): “The imaginary part of coherency (Nolte et al., 2004) is insensitive to phase synchronisation with no phase difference, and thus to the effects of signal leakage.”
MEG and EEG data analysis with MNE-Python (2013, background): “Examples of such measures are coherence, imaginary coherence (Nolte et al., 2004), and phase-locking value (PLV) (Lachaux et al., 1999).”
Nonlinear multivariate analysis of neurophysiological signals. (2005, background): “We refer the interested readers to (Essl and Rappelsberger, 1998; Nolte et al., 2004; Nunez et al., 1999; Nunez et al., 2001) for detailed treatments of these ideas.”
HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity (2013, background): “, volume conduction effects) occur with a certain time delay (Nolte et al. 2004).”
An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias (2011, positive): “Volume-conduction of source activity, and, in case of EEG (but not MEG) data, the use of a common reference, can spuriously inflate phase-synchronization indices (Fein et al., 1988; Nolte et al., 2004; Nunez and Srinivasan, 2006; Stam et al., 2007).”
The Virtual Brain: a simulator of primate brain network dynamics (2013, methodology): “In addition, there are specific implementations of the wavelet transform, complex coherence (Nolte et al., 2004; Freyer et al., 2012) and multiscale entropy (MSE) (Costa et al., 2002, 2005; Lake and Moorman, 2011).”
Diagnosis of Alzheimer's disease from EEG signals: where are we standing? (2010, background): “Examples of such measures are mean phase coherence [130], phase lag index [107], imaginary coherence [131], and global field synchrony (GFS) [132]; the first three measures are only applicable to pairs of signals, the latter allows us to detect phase coupling between a large number of signals.”
Event-related EEG time-frequency analysis: an overview of measures and an analysis of early gamma band phase locking in schizophrenia. (2008, background): “Some have even argued that modeling source activity does not eliminate the possibility that 2 sources are spuriously coherent because they are contaminated by activity from a third source.(51) Accordingly, other alternatives have been proposed such as examination of coherence between only the imagin”
A critical assessment of connectivity measures for EEG data: A simulation study (2013, background): “As a remedy, one can look at the imaginary part of coherency only (Nolte et al., 2004), which is systematically different from zero only for nonzero phase lags.”
A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls (2016, methodology): “When the complex-valued coherency is projected onto the imaginary axis (y-axis) we obtain the imaginary part of the coherency (Nolte et al., 2004).”
MNE software for processing MEG and EEG data (2013, background): “Examples of such measures are coherence, imaginary coherence (Nolte et al., 2004), and phase-locking value (PLV) (Lachaux et al., 1999).”
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.”
Frequency-dependent functional connectivity within resting-state networks: An atlas-based MEG beamformer solution (2012, positive): “, 2011a); ii) the development of functional connectivity estimators that are insensitive to these confounds of field spread and volume conduction (Nolte et al., 2004; Stam et al., 2007); iii) development of techniques such as DCM (Dynamic Causal Modelling; Friston et al.”