Investigations of dipole localization accuracy in MEG using the bootstrap

We describe the use of the nonparametric bootstrap to investigate the accuracy of current dipole localization from magnetoencephalography (MEG) studies of event related neural activity. The bootstrap is well suited to analysis of event-related MEG data since the experiments are often repeated 100 or more times and averaged to achieve acceptable SNRs. The set of repetitions or "epochs" can be viewed as a set of i.i.d. realizations of the brain\´s response to the experiment. Sampling from these epochs and averaging can generate bootstrap resamples. In this study we applied the bootstrap resampling technique to MEG data from a somatotopic experiment. Four fingers of the right and left hand of a healthy subject were electrically stimulated, and about 400 trials per stimulation were recorded and averaged in order to measure the somatotopic mapping of the fingers in the SI area of the brain. Based on the single trial recordings for each finger, we performed 5000 bootstrap resamples. We reconstructed dipoles from these resampled averages, using the RAP-MUSIC source localization algorithm. To find the correspondences between multiple sources in each resample dipoles with similar time-series and forward fields were assumed to represent the same source. These dipoles were then clustered using a GMM (Gaussian mixture model) clustering algorithm, using their combined normalized time-series and topography as feature vectors. The mean and standard deviation of the dipole position and the dipole time-series in each cluster were computed to provide estimates of the accuracy of the reconstructed source locations and time-series.