An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias

By Martin A. Vinck, Robert Oostenveld, Marijn van Wingerden, Francesco Battaglia, Cyriel M. A. Pennartz. NeuroImage, 2011

889 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/cf852fa3c05bae596e2fe5d7770bf61514ed6b23

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Frequency-dependent functional connectivity within resting-state networks: An atlas-based MEG beamformer solution (2012, positive): “Vinck et al. (2011) have recently described a promising modification of PLI with reduced underestimation of connectivity between sources with smalllag interactions, as well as a reduced estimator bias, although this comes at the expense of introducing an arbitrary bias favouring large phase…”
Graph analysis of functional brain networks: practical issues in translational neuroscience (2014, methodology): “Unbiased methods have been also developed for multivariate phase coupling measures [83] .”
Phase transfer entropy: A novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions (2014, background): “Such phase-based information flow from one cortical area to another cannot be evaluated using phase synchrony metrics (Rosenblum et al., 1996; Stam et al., 2007; Vinck et al., 2011) which are inherently undirected.”
A Note on the Phase Locking Value and its Properties (2013, background): “When computing synchrony between pairs of electrodes or cortical locations, nonzero PLVs can arise from a single source contributing to both signals as a result of either volume conduction in channel space or limited spatial resolution in the case of cortical current density maps (Amor et al., 2005;”
MNE software for processing MEG and EEG data (2013, methodology): “(Vinck et al., 2011) with multi-taper spectral estimation.”
HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity (2013, methodology): “Several indexes have been derived (Nolte et al., 2004, 2008; Stam, Nolte, & Daffertshofer, 2007; Vinck et al., 2011), which make use of this phase relationship to estimate the existence of true FC between x(t) and y(t).”
Human single-neuron activity is modulated by intracranial theta burst stimulation of the basolateral amygdala (2024, unknown): “Although certain metrics are not biased by sample size (e.g. pairwise phase consistency), low spike counts can dramatically affect variance and, therefore, result in unstable estimates ( Vinck et al., 2011 ).”
Abnormal Brain Connectivity Patterns in Children with Global Developmental Delay Accompanied by Cognitive Impairment: A Resting-State EEG Study. (2025, unknown): “…poor classification performance [20]. wPLI, which is based on the PLI, weights and scales phase differences according to their proximity to the real axis, further reduces volume conduction effects while more accurately depicting the “strength and direc-tion” of true functional connectivity [21].”
Distinct developmental changes in linear and nonlinear neural interactions across infancy and adulthood (2025, unknown): “…weighted Phase-Lag Index (WPLI) applied to electro-physiological signals (Canales-Johnson et al., 2021a; Olivares et al., 2025; Potash et al., 2025; Vinck et al., 2011) measures the extent to which phase angle di ff erences between two time series x ( t ) and y ( t ) are distributed towards…”
Hierarchical consciousness: the Nested Observer Windows model (2024, background): “A metric that captures this form of weak coupling is weighted phase lag index (wPLI) that quantifies a systematic non-zero phase lag between two independent signals (Vinck et al. 2011).”
Targeting neural oscillations with transcranial alternating current stimulation (2021, background): “Weighted phase lag index (wPLI) is a metric that capitalizes on the observation that genuine functional connectivity requires a consistent non-zero phase lag (Vinck, 2011).”
Causal role of cross-frequency coupling in distinct components of cognitive control (2021, background): “…the degree to which two signals have a consistent non-zero phase lag and this accounts for the confound of zero-lag phase lag connectivity that arises from volume conduction in EEG (Vinck et al., 2011) H is Hilbert transform, imag takes the imaginary component, t is time, N is number of time point.”
Dose-dependent power and connectivity modulation of low frequency oscillations through transcranial magnetic stimulation in non-human primates (2026, unknown): “Lastly, to investigate phase-based connectivity changes in response to TMS pulses, the debiased weighted phase lag index (dwPLI) calculated between the contact closest to the TMS coil (most anterior frontal contact) and all remaining contacts (66).”
Top-down and bottom-up interactions rely on nested brain oscillations to shape rhythmic visual attention sampling (2025, unknown): “Inter-areal connectivity was estimated in the sensor space between frontal and parieto-occipital electrodes of both hemi-spheres (extending the analyses of [36]) via the wPLI [78].”
The representation of abstract goals in working memory is supported by task-congruent neural geometry (2024, unknown): “We chose the metric of weighted phase lag index (wPLI) as it is more robust against artifacts dues to volume conduction and noise [71].”
Development of Artificial Intelligence for Determining Major Depressive Disorder Based on Resting-State EEG and Single-Pulse Transcranial Magnetic Stimulation-Evoked EEG Indices (2024, methodology): “Phase synchronization between each channel at 17 electrode sites in the frontal region was calculated using the weighted phase lag index (wPLI) [21–23] and averaged for each of the θ , α , β , and γ bands using the open source software FieldTrip toolbox (20221223) [24].”
Mental Activity as the Bridge between Neural Biomarkers and Symptoms of Psychiatric Illness (2022, methodology): “Functional connectivity analysis using EEG was enabled by the development of the (weighted) phase lag index, in which the confound of volume conduction was addressed by asserting that a genuine functional connection will exhibit a systematic phase lag due to conduction delay.(46,47) This approach re”