A Tutorial on Data-Driven Methods for Statistically Assessing ERP Topographies

Abstract

Dynamic changes in ERP topographies can be conveniently analyzed by means of microstates, the so-called "atoms of thoughts", that represent brief periods of quasi-stable synchronized network activation. Comparing temporal microstate features such as on- and offset or duration between groups and conditions therefore allows a precise assessment of the timing of cognitive processes. So far, this has been achieved by assigning the individual time-varying ERP maps to spatially defined microstate templates obtained from clustering the grand mean data into predetermined numbers of topographies (microstate prototypes). Features obtained from these individual assignments were then statistically compared. This has the problem that the individual noise dilutes the match between individual topographies and templates leading to lower statistical power. We therefore propose a randomization-based procedure that works without assigning grand-mean microstate prototypes to individual data. In addition, we propose a new criterion to select the optimal number of microstate prototypes based on cross-validation across subjects. After a formal introduction, the method is applied to a sample data set of an N400 experiment and to simulated data with varying signal-to-noise ratios, and the results are compared to existing methods. In a first comparison with previously employed statistical procedures, the new method showed an increased robustness to noise, and a higher sensitivity for more subtle effects of microstate timing. We conclude that the proposed method is well-suited for the assessment of timing differences in cognitive processes. The increased statistical power allows identifying more subtle effects, which is particularly important in small and scarce patient populations.