Mi Xichao, Cheng Ning, Zhang Tao
College of Life Sciences and Key Laboratory of Bioactive Materials, Ministry of Education, Nankai University, Tianjin 300071, PR China.
College of Life Sciences and Key Laboratory of Bioactive Materials, Ministry of Education, Nankai University, Tianjin 300071, PR China.
J Neurosci Methods. 2014 Apr 30;227:57-64. doi: 10.1016/j.jneumeth.2014.02.006. Epub 2014 Feb 15.
General partial directed coherence (gPDC) and permutation conditional mutual information (PCMI) have been widely used to analyze neural activities. These two algorithms are representative of linear and nonlinear methods, respectively. However, there is little known about the difference between their performances in measurements of neural information flow (NIF).
Comparison of these two approaches was effectively performed based on the neural mass model (NMM) and real local field potentials.
The results showed that the sensitivity of PCMI was more robust than that of gPDC. The coupling strengths calculated by PCMI were closer to theoretical values in the bidirectional mode of NMM. Furthermore, there was a small Coefficient of Variance (C.V.) for the PCMI results. The gPDC was more sensitive to alterations in the directionality index or the coupling strength of NMM; the gPDC method was more likely to detect a difference between two distinct types of coupling strengths compared to that of PCMI, and gPDC performed well in the identification of the coupling strength in the unidirectional mode.
COMPARISON TO EXISTING METHOD(S): A comparison between gPDC and PCMI was performed and the advantages of the approaches are discussed.
The performance of the PCMI is better than that of gPDC in measuring the characteristics of connectivity between neural populations. However, gPDC is recommended to distinguish the differences in connectivity between two states in the same pathway or to detect the coupling strength of the unidirectional mode, such as the hippocampal CA3-CA1 pathway.
广义偏相干（gPDC）和排列条件互信息（PCMI）已被广泛用于分析神经活动。这两种算法分别代表线性和非线性方法。然而，关于它们在神经信息流（NIF）测量中的性能差异，人们了解甚少。
基于神经质量模型（NMM）和实际局部场电位有效地对这两种方法进行了比较。
结果表明，PCMI的敏感性比gPDC更强。在NMM的双向模式下，由PCMI计算出的耦合强度更接近理论值。此外，PCMI结果的变异系数（C.V.）较小。gPDC对NMM的方向性指数或耦合强度的变化更敏感；与PCMI相比，gPDC方法更有可能检测到两种不同类型耦合强度之间的差异，并且gPDC在单向模式下耦合强度的识别方面表现良好。
对gPDC和PCMI进行了比较，并讨论了这些方法的优点。
在测量神经群体之间的连接特征方面，PCMI的性能优于gPDC。然而，建议使用gPDC来区分同一通路中两种状态之间的连接差异，或检测单向模式的耦合强度，如海马体CA3-CA1通路。
