BOOTSTRAP-BASED STATISTICAL THRESHOLDING FOR MEG SOURCE RECONSTRUCTION IMAGES

摘要

This paper proposes a bootstrap-based statistical method for extracting target source activities from MEG/EEG source reconstruction results. The method requires measurements in a control condition, which contains only non-target source activities. The method derives, at each pixel location, an empirical probability distribution of the non-target source activity using bootstrapped reconstruction obtained from the control period. The statistical threshold that can extract the target source activities is derived based on the empirical distributions obtained from all pixel locations. Here, the multiple comparison problem is taken into account by using two step procedure: studentizing these empirical distributions and deriving an empirical distribution of the maximum pseudo T value at each pixel location. The results of numerical experiments are presented to demonstrate the method’s effectiveness.