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On the interpretability and computational reliability of frequency-domain Granger causality

机译:关于频域格兰杰因果关系的可解释性和计算可靠性

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摘要

This Correspondence article is a comment which directly relates to the paper “A study of problems encountered in Granger causality analysis from a neuroscience perspective” (Stokes and Purdon, 2017). We agree that interpretation issues of Granger causality (GC) in neuroscience exist, partially due to the historically unfortunate use of the name “causality”, as described in previous literature. On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used) and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures based on a suboptimal use of them. Furthermore, since data from simulated systems are used, the pitfalls that are found with the used formulation are intended to be general, and not limited to neuroscience. It would be a pity if this paper, even if written in good faith, became a wildcard against all possible applications of GC, regardless of the large body of work recently published which aims to address faults in methodology and interpretation. In order to provide a balanced view, we replicate the simulations of Stokes and Purdon, using an updated GC implementation and exploiting the combination of spectral and causal information, showing that in this way the pitfalls are mitigated or directly solved.
机译:这篇对应评论的文章直接与论文“从神经科学角度研究格兰杰因果分析中遇到的问题”(Stokes和Purdon,2017)直接相关。我们同意,神经科学中存在格兰杰因果关系(GC)的解释问题,部分是由于历史上不幸地使用了“因果关系”这一名称,如先前文献所述。另一方面,我们认为Stokes和Purdon使用的GC公式已经过时(尽管仍在使用),并且没有充分考虑GC不同频域版本的潜力。在这样做时,他们的论文基于对它们的次优使用而取消了GC措施。此外,由于使用了来自模拟系统的数据,因此使用所使用的配方发现的陷阱是通用的,并不限于神经科学。如果即使真诚地写这篇论文成为反对GC的所有可能应用的通配符,不管最近发布的旨在解决方法论和解释错误的大量工作,将是遗憾的。为了提供一个平衡的视图,我们使用更新的GC实现并利用光谱和因果信息的组合来复制Stokes和Purdon的模拟,这表明以这种方式可以减轻或直接解决陷阱。

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