利用传递熵和互信息从时间序列推断网络特性：多变量与双变量方法的验证
Inferring network properties from time series using transfer entropy and mutual information: validation of multivariate versus bivariate approaches
摘要：
从时间序列中推断出的功能性和有效性网络是网络神经科学的核心。解释它们的性质需要推断的网络模型来反映关键的潜在结构特征;然而，即使是一些虚假的链接也会扭曲网络测量，挑战功能连接体。我们研究了在何种程度上可以推断的基础网络的微观和宏观性质的算法的基础上互信息和二元/多元传递熵。验证是在两个猕猴连接体和具有各种拓扑结构的合成网络（规则格，小世界，随机，无标度，模块化）上进行的。模拟是基于神经质量模型和自回归动力学（采用高斯估计直接比较功能连接和格兰杰因果关系）。我们发现，多变量传递熵捕获的关键属性的所有网络较长的时间序列。双变量方法可以在较短的时间序列中实现较高的召回率（灵敏度），但随着可用数据的增加，无法控制假阳性（较低的特异性）。这会导致高估集群、小世界和富俱乐部系数，低估最短路径长度和枢纽中心性，以及增加度分布尾部。因此，在解释通过相关性或成对统计依赖性测量获得的功能性连接体的网络特性时，而不是更全面（但需要大量数据）的多变量模型时，应谨慎使用。
英文摘要：
Functional and effective networks inferred from time series are at the core of network neuroscience. Interpreting their properties requires inferred network models to reflect key underlying structural features; however, even a few spurious links can distort network measures, challenging functional connectomes. We study the extent to which micro- and macroscopic properties of underlying networks can be inferred by algorithms based on mutual information and bivariate/multivariate transfer entropy. The validation is performed on two macaque connectomes and on synthetic networks with various topologies (regular lattice, small-world, random, scale-free, modular). Simulations are based on a neural mass model and on autoregressive dynamics (employing Gaussian estimators for direct comparison to functional connectivity and Granger causality). We find that multivariate transfer entropy captures key properties of all networks for longer time series. Bivariate methods can achieve higher recall (sensitivity) for shorter time series but are unable to control false positives (lower specificity) as available data increases. This leads to overestimated clustering, small-world, and rich-club coefficients, underestimated shortest path lengths and hub centrality, and fattened degree distribution tails. Caution should therefore be used when interpreting network properties of functional connectomes obtained via correlation or pairwise statistical dependence measures, rather than more holistic (yet data-hungry) multivariate models.
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