Adaptive thresholding for improving sensitivity in single-trial simultaneous EEG/fMRI

A common approach used to fuse simultaneously recorded EEG and fMRI is to correlate trial-by-trial variability in the EEG, or variability of components derived therefrom, with the blood oxygenation level dependent response. When this correlation is done using the conventional univariate approach, for example with the general linear model, there is the usual problem of correcting the statistics for multiple comparisons. Cluster thresholding is often used as the correction of choice, though in many cases it is utilized in an ad hoc way, for example by employing the same cluster thresholds for both traditional regressors (stimulus or behaviorally derived) and EEG-derived regressors. In this paper we describe a resampling procedure that takes into account the a priori statistics of the trial-to-trial variability of the EEG-derived regressors in a way that trades off cluster size and maximum voxel z-score to properly correct for multiple comparisons. We show that this data adaptive procedure improves sensitivity for smaller clusters of activation, without sacrificing the specificity of the results. Our results suggest that extra care is needed in correcting statistics when the regressor model is derived from noisy and/or uncertain measurements, as is the case for regressors constructed from single-trial variations in the EEG.