大象城南
(2022-02-27 21:46):
#paper doi:10.1016/j.neuroimage.2011.01.055 一种改进的对电生理数据计算信号之间相位同步方法——加权相位延迟指数,可以有效避免容积导电现象。这篇文章作者主要提出了一种更加鲁棒的功能连接度量方法。通常我们从LFP、EEG或MEG信号中测量神经元群之间的相互作用时,会采用诸如相位同步,相位相干的计算方法。然而由于空间分辨率并没有接近皮层下神经元的分布,且在头皮测量的EEG和MEG信号会经过颅骨,脑脊液衰减,这种会引起皮层下神经元群的信号在脑皮层测量的信号之间混杂着交互,从而使得度量真实的功能连接不准确。尽管之前有研究者提出虚部相干指数和相位延迟指数,但其要么无法准确度量噪声无关的信号相位的延迟或超前,要么对一些小的相位扰动不敏感,此外也会受到样本量大小产生偏差。为了解决这个问题,作者提出了加权的相位延迟指数具有无偏性,比前人提出的指标更能有效避免容积导电现象。目前该文章被引用900多次。
An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias
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Abstract:
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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