Institut de Neurosciences de la Timone-INT, UMR 7289 CNRS, Aix Marseille University, Campus de Santé Timone, 27 Bd. Jean Moulin, 13385 Marseille, France.
Comput Math Methods Med. 2012;2012:697610. doi: 10.1155/2012/697610. Epub 2012 May 10.
Granger causality analysis is becoming central for the analysis of interactions between neural populations and oscillatory networks. However, it is currently unclear whether single-trial estimates of Granger causality spectra can be used reliably to assess directional influence. We addressed this issue by combining single-trial Granger causality spectra with statistical inference based on general linear models. The approach was assessed on synthetic and neurophysiological data. Synthetic bivariate data was generated using two autoregressive processes with unidirectional coupling. We simulated two hypothetical experimental conditions: the first mimicked a constant and unidirectional coupling, whereas the second modelled a linear increase in coupling across trials. The statistical analysis of single-trial Granger causality spectra, based on t-tests and linear regression, successfully recovered the underlying pattern of directional influence. In addition, we characterised the minimum number of trials and coupling strengths required for significant detection of directionality. Finally, we demonstrated the relevance for neurophysiology by analysing two local field potentials (LFPs) simultaneously recorded from the prefrontal and premotor cortices of a macaque monkey performing a conditional visuomotor task. Our results suggest that the combination of single-trial Granger causality spectra and statistical inference provides a valuable tool for the analysis of large-scale cortical networks and brain connectivity.
格兰杰因果关系分析在分析神经元群体和振荡网络之间的相互作用方面变得至关重要。然而，目前尚不清楚单次试验估计的格兰杰因果关系谱是否可以可靠地用于评估方向影响。我们通过将单次试验格兰杰因果关系谱与基于广义线性模型的统计推断相结合来解决这个问题。该方法在合成和神经生理学数据上进行了评估。使用具有单向耦合的两个自回归过程生成二元合成数据。我们模拟了两种假设的实验条件：第一种模拟了恒定的单向耦合，而第二种模拟了耦合随试验的线性增加。基于 t 检验和线性回归的单次试验格兰杰因果关系谱的统计分析成功地恢复了方向影响的潜在模式。此外，我们还确定了检测方向性所需的最小试验次数和耦合强度。最后，我们通过分析执行条件视觉运动任务的猕猴的前额叶和运动前皮质同时记录的两个局部场电位 (LFP)，证明了其在神经生理学中的相关性。我们的结果表明，单次试验格兰杰因果关系谱和统计推断的结合为分析大规模皮质网络和大脑连接提供了一种有价值的工具。
