Identifying Granger causal relationships between neural power dynamics and variables of interest
Identifying Granger causal relationships between neural power dynamics and variables of interest
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DOI：
10.1016/j.neuroimage.2014.12.059
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发表时间：
2015-05-01
期刊：
影响因子：
5.7
通讯作者：
Daehne, Sven
中科院分区：
文献类型：
--
作者：
Winkler, Irene;Haufe, Stefan;Daehne, Sven
脑电图和脑磁图 (EEG/MEG) 信号振荡的功率调制与多种大脑功能有关。迄今为止，大多数证据都是通过将频带功率波动与特定目标变量（例如反应时间或任务评级）相关联来获得的，而振荡活动和行为之间的因果关系仍然不太清楚。在这里，我们建议通过格兰杰因果关系的统计概念来识别因果关系，并研究哪种方法最适合揭示大脑振荡功率和实验变量之间的格兰杰因果关系。作为在传感器层面测试此类因果关系的替代方案，我们建议线性组合每个传感器中包含的信息，以创建虚拟通道，对应于潜在大脑振荡的估计，可以评估其格兰杰因果关系。传感器的这种线性组合可以通过源分离方法给出，例如独立分量分析（ICA）或最近开发的源功率相关（SPoC）方法。这里我们比较直接从i）传感器获得的功率动态的格兰杰因果分析，ii）不针对格兰杰因果关系进行优化的空间滤波方法（ICA和SPoC），以及iii）直接优化空间滤波器以提取最大格兰杰导致给定目标的功率动态的源的方法变量。我们将这种方法称为格兰杰因果功率分析（GrangerCPA）。使用模拟和真实的脑电图记录，我们发现计算通道频谱功率的格兰杰因果关系会因体积传导而受到较差的信噪比的影响，而所有三种多元方法都缓解了这个问题。在受试者进行自定步调的足部运动的真实脑电图记录中，所有三种多变量方法都以相似的表现水平识别具有运动相关模式的神经振荡。在听觉感知任务中，与传统方法相比，GrangerCPA 的应用揭示了更多受试者的 α 振荡和反应时间之间的显着格兰杰因果关系。 (C) 2015 Elsevier Inc. 保留所有权利。
Power modulations of oscillations in electro-and magnetoencephalographic (EEG/MEG) signals have been linked to a wide range of brain functions. To date, most of the evidence is obtained by correlating bandpower fluctuations to specific target variables such as reaction times or task ratings, while the causal links between oscillatory activity and behavior remain less clear. Here, we propose to identify causal relationships by the statistical concept of Granger causality, and we investigate which methods are bests suited to reveal Granger causal links between the power of brain oscillations and experimental variables.As an alternative to testing such causal links on the sensor level, we propose to linearly combine the information contained in each sensor in order to create virtual channels, corresponding to estimates of underlying brain oscillations, the Granger-causal relations of which may be assessed. Such linear combinations of sensor can be given by source separation methods such as, for example, Independent Component Analysis (ICA) or by the recently developed Source Power Correlation (SPoC) method.Here we compare Granger causal analysis on power dynamics obtained fromi) sensor directly, ii) spatial filtering methods that do not optimize for Granger causality (ICA and SPoC), and iii) a method that directly optimizes spatial filters to extract sources the power dynamics of which maximally Granger causes a given target variable. We refer to this method as Granger Causal Power Analysis (GrangerCPA).Using both simulated and real EEG recordings, we find that computing Granger causality on channel-wise spectral power suffers from a poor signal-to-noise ratio due to volume conduction, while all three multivariate approaches alleviate this issue. In real EEG recordings from subjects performing self-paced foot movements, all three multivariate methods identify neural oscillations with motor-related patterns at a similar performance level. In an auditory perception task, the application of GrangerCPA reveals significant Granger-causal links between alpha oscillations and reaction times in more subjects compared to conventional methods. (C) 2015 Elsevier Inc. All rights reserved.