Shao Pei-Chiang, Huang Jian-Jia, Shann Wei-Chang, Yen Chen-Tung, Tsai Meng-Li, Yen Chien-Chang
Department of Mathematics, National Central University, Jhongli, 32001, Taiwan.
J Comput Neurosci. 2015 Jun;38(3):483-97. doi: 10.1007/s10827-015-0550-z. Epub 2015 Mar 13.
Granger causality (GC) analysis has emerged as a powerful analytical method for estimating the causal relationship among various types of neural activity data. However, two problems remain not very clear and further researches are needed: (1) The GC measure is designed to be nonnegative in its original form, lacking of the trait for differentiating the effects of excitations and inhibitions between neurons. (2) How is the estimated causality related to the underlying synaptic weights? Based on the GC, we propose a computational algorithm under a best linear predictor assumption for analyzing neuronal networks by estimating the synaptic weights among them. Under this assumption, the GC analysis can be extended to measure both excitatory and inhibitory effects between neurons. The method was examined by three sorts of simulated networks: those with linear, almost linear, and nonlinear network structures. The method was also illustrated to analyze real spike train data from the anterior cingulate cortex (ACC) and the striatum (STR). The results showed, under the quinpirole administration, the significant existence of excitatory effects inside the ACC, excitatory effects from the ACC to the STR, and inhibitory effects inside the STR.
格兰杰因果关系（GC）分析已成为一种强大的分析方法，用于估计各类神经活动数据之间的因果关系。然而，有两个问题仍不太明确，需要进一步研究：（1）GC测度在其原始形式中被设计为非负，缺乏区分神经元之间兴奋和抑制作用的特性。（2）估计的因果关系与潜在的突触权重有何关联？基于GC，我们在最佳线性预测器假设下提出一种计算算法，通过估计神经元网络之间的突触权重来分析神经元网络。在此假设下，GC分析可以扩展到测量神经元之间的兴奋和抑制作用。该方法通过三种模拟网络进行检验：具有线性、近似线性和非线性网络结构的网络。该方法还被用于分析来自前扣带回皮质（ACC）和纹状体（STR）的真实尖峰序列数据。结果表明，在给予喹吡罗的情况下，ACC内部存在显著的兴奋作用，从ACC到STR的兴奋作用，以及STR内部的抑制作用。
