Comparison of Phase Synchronization Measures for Identifying Stimulus-Induced Functional Connectivity in Human Magnetoencephalographic and Simulated Data
- PMID: 32636735
- PMCID: PMC7318889
- DOI: 10.3389/fnins.2020.00648
Comparison of Phase Synchronization Measures for Identifying Stimulus-Induced Functional Connectivity in Human Magnetoencephalographic and Simulated Data
Abstract
Phase synchronization measures are widely used for investigating inter-regional functional connectivity (FC) of brain oscillations, but which phase synchronization measure should be chosen for a given experiment remains unclear. Using neuromagnetic brain signals recorded from healthy participants during somatosensory stimuli, we compared the performance of four phase synchronization measures, imaginary part of phase-locking value, imaginary part of coherency (ImCoh), phase lag index and weighted phase lag index (wPLI), for detecting stimulus-induced FCs between the contralateral primary and ipsilateral secondary somatosensory cortices. The analyses revealed that ImCoh exhibited the best performance for detecting stimulus-induced FCs, followed by the wPLI. We found that amplitude weighting, which is related to computing both ImCoh and wPLI, effectively attenuated the influence of noise contamination. A simulation study modeling noise-contaminated periodograms replicated these findings. The present results suggest that the amplitude-dependent measures, ImCoh followed by wPLI, may have the advantage in detecting stimulus-induced FCs.
Keywords: amplitude coherence; cross-periodogram; functional connectivity; phase synchronization; somatosensory system.
Copyright © 2020 Yoshinaga, Matsuhashi, Mima, Fukuyama, Takahashi, Hanakawa and Ikeda.
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References
-
- Almeida M., Bioucas-Dias J., Vigário R. (2013). Separation of phase-locked sources in pseudo-real MEG data. EURASIP J. Adv. Signal Process. 2013:32 10.1186/1687-6180-2013-32 - DOI
-
- Benjamini Y., Hochberg Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57 289–300. 10.1111/j.2517-6161.1995.tb02031.x - DOI
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