背景:时间序列分析对于理解大脑信号及其与行为和认知的关系至关重要。基于聚类的排列测试(CBPT)通常用于分析各种电生理信号,包括EEG,MEG,ECoG,和sEEG数据,没有关于特定时间效应的先验假设。然而,CBPT的两个主要局限性包括无法直接分析具有多个固定效应的实验,以及无法解释随机效应(例如受试者之间的变异性).这里,我们提出了一种灵活的多步骤假设检验策略,使用CBPT与线性混合效应模型(LME)和广义线性混合效应模型(GLME),可以应用于广泛的实验设计和数据类型。
方法:我们首先使用模拟数据分布评估LME和GLME的统计稳健性。第二,我们应用多步假设检验策略来分析从简单的图像观察实验中收集的人类ECoG记录中提取的ERP和宽带功率信号,其中图像类别和新颖性作为固定效果。第三,我们通过模拟宽带功率信号的仿真,评估了使用LME的CBPT分析信号与使用单独的t检验在每个固定效应上运行的CBPT分析信号之间的统计功率差异。最后,我们将使用GLME的CBPT应用于高伽马爆发数据,以证明所提出的方法在非线性数据分析中的扩展。
结果:首先,我们发现LME和GLME是稳健的统计模型。在简单的模拟中,LME与其他适当应用的线性统计模型产生了高度一致的结果,但是LME在“次优”数据分析中的表现优于许多线性统计模型,并且比使用单独的t检验分析单个固定效应的能力更好。GLME也与其他非线性统计模型相似。第二,在现实世界的人类ECoG数据中,当应用于预定义的时间窗口或与CBPT结合使用时,LME至少执行了单独的t检验。此外,使用LME从单个类别选择通道中发现的伪种群复制潜伏期效应的组水平模型中使用CBPT提取的固定效应时程。第三,对模拟宽带功率信号的分析表明,使用LME的CBPT优于使用单独的t检验的CBPT,以识别具有显着的固定效应的时间窗口,尤其是对于小效应大小。最后,使用CBPT和GLME对高伽马爆发数据的分析产生的结果与使用应用于宽带功率数据的LME的CBPT一致。
结论:我们提出了一种使用CBPT结合LME和GLME进行电生理数据统计分析的通用方法。我们证明了该方法对于具有多个固定效应的实验是可靠的,并且适用于线性和非线性数据的分析。我们的方法最大化了跨多个实验变量的数据集中可用的统计能力,同时考虑了分层随机效应并控制了固定效应的FWER。这种方法实质上提高了功率,导致更好的再现性。此外,使用LME和GLME的CBPT可用于分析单个通道或伪群体数据,以比较功能或解剖数据组。
BACKGROUND: Time series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types.
METHODS: We first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data.
RESULTS: First, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of \"suboptimal\" data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data.
CONCLUSIONS: We propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data.