Granger causality revisited.
Granger causality revisited.
复制标题
DOI：
10.1016/j.neuroimage.2014.06.062
复制
发表时间：
2014-11-01
期刊：
影响因子：
5.7
通讯作者：
Litvak V
中科院分区：
文献类型：
--
作者：
Friston KJ;Bastos AM;Oswal A;van Wijk B;Richter C;Litvak V
这篇技术论文提供了一个关键的重新评估（光谱）格兰杰因果关系措施在生物时间序列的分析。利用耦合神经元动力学的真实（神经质量）模型，我们评估了参数和非参数格兰杰因果关系的鲁棒性。从一类广泛的神经元动力学生成（状态空间）模型开始，我们展示了它们的Volterra核如何规定它们对随机波动响应的二阶统计量；以交叉谱密度、交叉协方差、自回归系数和有向传递函数为特征。这些量反过来指定格兰杰因果关系——在生成模型的参数和预期的格兰杰因果关系之间提供直接的（解析的）联系。我们使用这个链接来表明，当潜在的动力学由缓慢（不稳定）模式主导时，基于自回归模型的格兰杰因果关系度量可能变得不可靠——正如主李雅普诺夫指数所量化的那样。然而，基于因果谱因子的非参数测度对动态不稳定性具有鲁棒性。然后，我们演示了参数和非参数频谱因果关系测量如何在测量噪声存在的情况下变得不可靠。最后，我们证明了这个问题可以通过从Volterra核中导出光谱因果度量来处理，使用动态因果建模来估计。本文描述了期望格兰杰因果测度的评价。它使用这些措施来量化具有动力不稳定性和噪声的问题。这些问题可以通过基于DCM估计的格兰杰测度来解决。
This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality. Starting from a broad class of generative (state-space) models of neuronal dynamics, we show how their Volterra kernels prescribe the second-order statistics of their response to random fluctuations; characterised in terms of cross-spectral density, cross-covariance, autoregressive coefficients and directed transfer functions. These quantities in turn specify Granger causality — providing a direct (analytic) link between the parameters of a generative model and the expected Granger causality. We use this link to show that Granger causality measures based upon autoregressive models can become unreliable when the underlying dynamics is dominated by slow (unstable) modes — as quantified by the principal Lyapunov exponent. However, nonparametric measures based on causal spectral factors are robust to dynamical instability. We then demonstrate how both parametric and nonparametric spectral causality measures can become unreliable in the presence of measurement noise. Finally, we show that this problem can be finessed by deriving spectral causality measures from Volterra kernels, estimated using dynamic causal modelling. This paper describes the evaluation of expected Granger causality measures. It uses these measures to quantify problems with dynamical instability and noise. These problems are resolved by basing Granger measures on DCM estimates.
登录
查看更多内容
影响因子：
16.2
作者：
Bosman CA;Schoffelen JM;Brunet N;Oostenveld R;Bastos AM;Womelsdorf T;Rubehn B;Stieglitz T;De Weerd P;Fries P
通讯作者：
Fries P
影响因子：
16.2
作者：
Bastos AM;Usrey WM;Adams RA;Mangun GR;Fries P;Friston KJ
通讯作者：
Friston KJ
DOI：
10.1073/pnas.1011284108
发表时间：
2011-07-05
影响因子：
11.1
作者：
Buffalo, Elizabeth A.;Fries, Pascal;Desimone, Robert
通讯作者：
Desimone, Robert
影响因子：
2.4
作者：
Lindner M;Vicente R;Priesemann V;Wibral M
通讯作者：
Wibral M
影响因子：
5.7
作者：
Moran, R. J.;Stephan, K. E.;Kiebel, S. J.;Rombach, N.;O'Connor, W. T.;Murphy, K. J.;Reilly, R. B.;Friston, K. J.
通讯作者：
Friston, K. J.