通过谱传递熵测量脑网络节点间的信息传递
Measuring Information Transfer Between Nodes in a Brain Network through Spectral Transfer Entropy
AI总结：
本文提出频域谱传递熵STE及基于藤copula的估计与检验方法，用于量化脑电通道特定频段振荡间的信息流，并通过数值实验和视觉记忆EEG分析验证其优势。
摘要：
脑连接刻画了静息状态或执行认知任务期间脑网络不同区域之间的相互作用。在研究脑电图（EEG）等脑信号时，研究连接性的一种形式化方法是采用一种称为传递熵（TE）的信息论因果测度。为增强TE在脑信号分析中的功能，我们提出了一种在频域捕捉跨通道信息传递的新方法。具体而言，我们引入了一种新的测度——谱传递熵（STE），用于量化从一个通道的特定频段振荡到另一个通道的特定频段振荡的信息流大小与方向。我们所提方法的主要优势在于，它以一种新颖的方式表述TE，从而对特定频段振荡进行推断，同时对滤波相关的固有问题保持稳健。此外，STE的一个优点是允许针对多重比较进行调整，以控制假阳性率。另一项新颖贡献是利用藤copula理论提出了一种简单而高效的STE估计方法。该方法能够产生精确为零的STE估计值（即参数空间的边界点），而无需进行偏差调整。借助藤copula表示，定义了一个表现出零STE的零copula模型，从而能够通过标准重采样直接进行显著性检验。最后，我们通过数值实验证明了所提STE测度的优势，并在一项视觉记忆实验的EEG数据分析中给出了有趣且新颖的发现。
英文摘要：
Brain connectivity characterizes interactions between different regions of a brain network during resting-state or performance of a cognitive task. In studying brain signals such as electroencephalograms (EEG), one formal approach to investigating connectivity is through an information-theoretic causal measure called transfer entropy (TE). To enhance the functionality of TE in brain signal analysis, we propose a novel methodology that captures cross-channel information transfer in the frequency domain. Specifically, we introduce a new measure, the spectral transfer entropy (STE), to quantify the magnitude and direction of information flow from a band-specific oscillation of one channel to another band-specific oscillation of another channel. The main advantage of our proposed approach is that it formulates TE in a novel way to perform inference on band-specific oscillations while maintaining robustness to the inherent problems associated with filtering. In addition, an advantage of STE is that it allows adjustments for multiple comparisons to control false positive rates. Another novel contribution is a simple yet efficient method for estimating STE using vine copula theory. This method can produce an exact zero estimate of STE (which is the boundary point of the parameter space) without the need for bias adjustments. With the vine copula representation, a null copula model, which exhibits zero STE, is defined, thus enabling straightforward significance testing through standard resampling. Lastly, we demonstrate the advantage of the proposed STE measure through numerical experiments and provide interesting and novel findings on the analysis of EEG data in a visual-memory experiment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
