Measuring Information Transfer Between Nodes in a Brain Network through Spectral Transfer Entropy
通过谱转移熵测量脑网络节点间的信息传递AI 解读 ↓
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通过谱转移熵测量脑网络节点间的信息传递
- 研究问题
- 脑电信号的不同通道和频带之间可能存在有方向的信息传递；研究如何在频域量化这些联系并开展显著性推断。
- 方法与思路
- 作者提出谱转移熵，以 vine copula 方法估计，构造零转移熵的原假设模型，并利用重抽样检验及多重比较调整。
- 材料陈述的贡献
- 摘要称，该方法可得到恰为零的边界估计而无需偏差调整，并通过数值实验和视觉记忆任务脑电数据展示应用表现。
- 阅读关注点
- 应核对频带信息传递的定义、对滤波问题的稳健性依据，以及零假设重抽样与多重比较程序的有效条件。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；信息传递指标的方向性不能仅凭摘要解释为干预因果效应。
原文摘要
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.