School of ECE, College of Engineering, University of Tehran, Tehran, Iran.
Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA; Neuroscience Institute, Le Bonheur Children's Hospital, Memphis, TN, USA.
J Neurosci Methods. 2018 Oct 1;308:317-329. doi: 10.1016/j.jneumeth.2018.08.026. Epub 2018 Sep 3.
The effects of statistical testing on the results of multivariate autoregressive (MVAR)-based effective connectivity analysis have not been adequately investigated, and it is still unclear which statistical test can provide the most accurate results.
Using simulated and real electrocorticographic (ECoG) data, we investigated the performance of three nonparametric statistical tests - Monte Carlo permutation, bootstrap resampling, and surrogate data method in MVAR-based effective connectivity analysis. Receiver operating characteristic (ROC) analysis and area under the ROC curve (AUC) were used to assess the performance of each statistical test method. In addition, we found optimal p-values for each method based on ROC analysis. Finally, we investigated the application of statistical tests on partial directed coherence analysis of ECoG data collected in a patient with epilepsy.
The bootstrap statistical test performed more accurately than other methods. The surrogate method slightly outperformed the Monte Carlo permutation method. Optimal p-values of statistical tests depended on signal-to-noise ratio (SNR) of data, and its value increased by reducing SNR of data. By considering the typical SNR range of electrophysiological data, we recommended an optimal p-value range for the application of each statistical test method.
Limited studies have investigated the performance of statistical tests for MVAR-based effective connectivity analysis. For the first time, we have investigated the effects of baseline connections on the various performances of statistical tests.
We recommend utilizing the bootstrap statistical test with p-value between 0.05 and 0.1 for effective connectivity analysis of ECoG data.
统计检验对基于多变量自回归（MVAR）的有效连通性分析结果的影响尚未得到充分研究，目前仍不清楚哪种统计检验能提供最准确的结果。
使用模拟和真实的脑电（ECoG）数据，我们研究了三种非参数统计检验——蒙特卡罗置换、自举重采样和替代数据方法在基于 MVAR 的有效连通性分析中的性能。接收者操作特征（ROC）分析和 ROC 曲线下面积（AUC）用于评估每种统计检验方法的性能。此外，我们还根据 ROC 分析找到了每种方法的最佳 p 值。最后，我们研究了统计检验在癫痫患者 ECoG 数据的偏定向相干分析中的应用。
自举统计检验的性能优于其他方法。替代方法略优于蒙特卡罗置换方法。统计检验的最佳 p 值取决于数据的信噪比（SNR），且其值随数据 SNR 的降低而增加。考虑到电生理数据的典型 SNR 范围，我们为每种统计检验方法的应用推荐了一个最佳的 p 值范围。
已有研究有限地调查了统计检验在基于 MVAR 的有效连通性分析中的性能。我们首次研究了基线连接对各种统计检验性能的影响。
我们建议在 ECoG 数据的有效连通性分析中使用自举统计检验，p 值在 0.05 到 0.1 之间。
