Performance comparison between gPDC and PCMI for measuring directionality of neural information flow.

摘要: Abstract Background 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 nonlinear methods, respectively. However, there is little known about the difference between their performances in measurements flow (NIF). New method Comparison these approaches was effectively performed based on mass model (NMM) real local field potentials. Results The results showed that sensitivity PCMI more robust than gPDC. coupling strengths calculated by were closer theoretical values bidirectional mode NMM. Furthermore, a small Coefficient Variance (C.V.) for results. gPDC sensitive alterations directionality index or strength NMM; likely detect distinct types compared PCMI, well identification unidirectional mode. existing method(s) A comparison advantages discussed. Conclusions performance better measuring characteristics connectivity populations. recommended distinguish differences states same pathway mode, such as hippocampal CA3–CA1 pathway.