An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias
Open accessAbstract
Phase-synchronization is a manifestation of interaction between neuronal groups measurable from LFP, EEG or MEG signals, however, volume conduction can cause the coherence and the phase locking value to spuriously increase. It has been shown that the imaginary component of the coherency (ImC) cannot be spuriously increased by volume-conduction of independent sources. Recently, it was proposed that the phase lag index (PLI), which estimates to what extent the phase leads and lags between signals from two sensors are nonequiprobable, improves on the ImC. Compared to ImC, PLI has the advantage of being less influenced by phase delays. However, sensitivity to volume-conduction and noise, and capacity to detect changes in phase-synchronization, is hindered by the discontinuity of the PLI, as small perturbations turn phase lags into leads and vice versa. To solve this problem, we introduce a related index, namely the weighted phase lag index (WPLI). Differently from PLI, in WPLI the contribution of the observed phase leads and lags is weighted by the magnitude of the imaginary component of the cross-spectrum. We demonstrate two advantages of the WPLI over the PLI, in terms of reduced sensitivity to additional, uncorrelated noise sources and increased statistical power to detect changes in phase-synchronization. Another factor that can affect phase-synchronization indices is sample-size bias. We show that, when directly estimated, both PLI and the magnitude of the ImC have typically positively biased estimators. To solve this problem, we develop an unbiased estimator of the squared PLI, and a debiased estimator of the squared WPLI.
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The wPLI is a good candidate parameter for revealing synchronized data among connectivity analyses of EEG and magneto-encephalogram signals.
“In conclusion, among the connectivity analyses of EEG and magneto-encephalogram signals, the wPLI is a good candidate parameter for revealing synchronized data [36].”
From QEEG Signatures are Associated with Nonmotor Dysfunctions in Parkinson's Disease and Atypical Parkinsonism: An Integrative Analysis (2023) · cites this paper as [36]
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- Authors
- Vinck, M., Oostenveld, R., van Wingerden, M., Battaglia, F., & Pennartz, C. M. A.
- Affiliations
- University of Amsterdam (NL)
- Radboud University Nijmegen (NL)
- Corresponding author
- Vinck, Martin
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2011-04
- Volume
- 55
- Issue
- 4
- Pages
- 1548-1565
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1966641148
- Cited by
- 1,753
- References
- 44
- Citations by year
- 2026: 257 · 2025: 238 · 2024: 173 · 2023: 141 · 2022: 150 · 2021: 160 · 2020: 143 · 2019: 134 · 2018: 89 · 2017: 78 · 2016: 61 · 2015: 33 · 2014: 45 · 2013: 29 · 2012: 17
- FWCI
- 15.42
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- BrainGain Smart Mix Programme of the Netherlands Ministry of Economic Affairs
- Netherlands Ministry of Education, Culture and Science
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- phase synchronization, phase lag index, weighted phase lag index, volume conduction, noise sensitivity, sample size bias, unbiased estimation, statistical power, cross-spectral density, electrophysiological signals
- MeSH terms
- Algorithms, Brain, Brain Mapping, Humans, Neural Conduction, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Artifacts, Sample Size
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