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首页> 外文期刊>NeuroImage >Horizontal visibility graph transfer entropy (HVG-TE): A novel metric to characterize directed connectivity in large-scale brain networks
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Horizontal visibility graph transfer entropy (HVG-TE): A novel metric to characterize directed connectivity in large-scale brain networks

机译：水平可见性图形传输熵（HVG-TE）：一种新型度量，以表征大型大脑网络中的定向连接

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Abstract We propose a new measure, horizontal visibility graph transfer entropy (HVG-TE), to estimate the direction of information flow between pairs of time series. HVG-TE quantifies the transfer entropy between the degree sequences of horizontal visibility graphs derived from original time series. Twenty-one R?ssler attractors unidirectionally coupled in the posterior-to-anterior direction were used to simulate 21-channel Electroencephalography (EEG) brain networks and validate the performance of the HVG-TE. We showed that the HVG-TE is robust to different levels of coupling strengths between the coupled R?ssler attractors, a wide range of time delays, different sample sizes, the effects of noise and linear mixing, and the choice of reference for EEG data. We also applied HVG-TE to EEG data in 20 healthy controls and compared its performance to a recently introduces phase-based TE measure (PTE). We found that compared with PTE, HVG-TE consistently detected stronger posterior-to-anterior information flow patterns in the alpha-band (8–13 Hz) EEG brain networks for three different references. Moreover, in contrast to PTE, HVG-TE does not require an assumption on the periodicity of input signals, therefore it can be more widely applicable, even for non-periodic signals. This study shows that the HVG-TE is a directed connectivity measure to characterise the direction of information flow in large-scale brain networks. Highlights ? We propose a new directed connectivity measure to estimate the direction of information flow between pairs of time series. ? HVG-TE was found to be robust to the effects of coupling strength, time delay, sample size, noise and linear mixing. ? HVG-TE could consistently detect strong posterior-to-anterior information flow patterns in EEG brain networks for different references.
机译：摘要我们提出了一种新的度量，水平可见性图形转移熵（HVG-TE），以估算一对时间序列之间的信息流程。 HVG-TE量化了从原始时间序列衍生的水平可见性图之间的度序列之间的转移熵。二十一r？在后到前方向上单向耦合的二十一r？Ssler吸引器用于模拟21通道脑电图（EEG）脑网络并验证HVG-TE的性能。我们认为HVG-TE对耦合的R耦合耦合耦合强度的耦合强度，各个时间延迟，不同的样本尺寸，噪声和线性混合影响以及EEG数据的参考选择的鲁棒。我们还在20个健康控制中将HVG-TE应用于EEG数据，并将其性能与最近引入了基于阶段的TE测量（PTE）。我们发现与PTE相比，HVG-TE始终检测到α带（8-13Hz）EEG脑网络中的更强的后向信息流模式进行三种不同的参考。此外，与PTE形成对比，HVG-TE不需要对输入信号的周期性的假设，因此它可以更广泛适用，即使对于非周期性信号。本研究表明，HVG-TE是指向的连接度量，以表征大规模脑网络中信息流方向。强调 ？我们提出了一种新的定向连接度量来估计一对时间序列之间的信息流程。还是发现HVG-TE对偶联强度，时间延迟，样品尺寸，噪声和线性混合的影响具有鲁棒。还是HVG-TE可以始终如时检测EEG脑网络中的强往前信息流模式以进行不同的参考。

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