Flexible Multi-Step Hypothesis Testing of Human ECoG Data using Cluster-based Permutation Tests with GLMEs.

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 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