Objective: Assessing brain connectivity from electrophysiological signals is of great relevance in neuroscience, but results are still debated and depend crucially on how connectivity is defined and on mathematical instruments utilized. Aim of this work is to assess the capacity of bivariate Transfer Entropy (TE) to evaluate connectivity, using data generated from simple neural mass models of connected Regions of Interest (ROIs). Approach: Signals simulating mean field potentials were generated assuming two, three or four ROIs, connected via excitatory or by-synaptic inhibitory links. We investigated whether the presence of a statistically significant connection can be detected and if connection strength can be quantified. Main Results: Results suggest that TE can reliably estimate the strength of connectivity if neural populations work in their linear regions, and if the epoch lengths are longer than 10 s. In case of multivariate networks, some spurious connections can emerge (i.e., a statistically significant TE even in the absence of a true connection); however, quite a good correlation between TE and synaptic strength is still preserved. Moreover, TE appears more robust for distal regions (longer delays) compared with proximal regions (smaller delays): an approximate a priori knowledge on this delay can improve the procedure. Finally, non-linear phenomena affect the assessment of connectivity, since they may significantly reduce TE estimation: information transmission between two ROIs may be weak, due to non-linear phenomena, even if a strong causal connection is present. Significance: Changes in functional connectivity during different tasks or brain conditions, might not always reflect a true change in the connecting network, but rather a change in information transmission. A limitation of the work is the use of bivariate TE. In perspective, the use of multivariate TE can improve estimation and reduce some of the problems encountered in the present study.

中文翻译：

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转移熵作为大脑连通性的衡量标准：借助神经质量模型的批判性分析

目标：从电生理信号评估大脑连通性在神经科学中具有重要意义，但结果仍然存在争议，并且关键取决于连通性的定义方式和数学工具的使用。这项工作的目的是使用从连接的感兴趣区域 (ROI) 的简单神经质量模型生成的数据来评估双变量传递熵 (TE) 评估连通性的能力。方法：假设有两个、三个或四个 ROI，通过兴奋性或突触抑制性链接连接，生成模拟平均场电位的信号。我们调查了是否可以检测到具有统计意义的连接的存在以及是否可以量化连接强度。主要结果：结果表明，如果神经群体在其线性区域内工作，并且纪元长度超过 10 秒，则 TE 可以可靠地估计连接强度。在多元网络的情况下，可能会出现一些虚假连接（即，即使在没有真正连接的情况下，也具有统计显着性的 TE）；然而，TE 和突触强度之间的相关性仍然很好。此外，与近端区域（较小延迟）相比，TE 对远端区域（延迟较长）似乎更稳健：对这种延迟的近似先验知识可以改进程序。最后，非线性现象会影响连通性的评估，因为它们可能会显着降低 TE 估计：由于非线性现象，两个 ROI 之间的信息传输可能很弱，即使存在很强的因果关系。意义：在不同任务或大脑状况期间功能连接的变化，可能并不总是反映连接网络的真实变化，而是信息传输的变化。这项工作的一个限制是使用双变量 TE。从长远来看，使用多元 TE 可以改进估计并减少当前研究中遇到的一些问题。