Detection of power changes between conditions using split-half resampling of synthetic aperture magnetometry data.

Synthetic Aperture Magnetometry (SAM) measures changes in task-related power using pseudo-t values which are affected by changes in both signal and noise. Detecting significant signal power changes between two separate experimental conditions should not be done directly due to possible fluctuation in the noise as well as the response. This study proposes a method to estimate the noise within a single condition, which is then used to test the null hypothesis of no difference between the conditions. The noise estimation is based on a split-half resampling technique. For each resampling, the data of a given condition is divided into two halves. The difference of the pseudo-t volumes between the pair of the datasets is calculated. After multiple resamplings, the confidence limits of the differences within this single condition are computed for a given p-value so that one can test the null hypotheses that the second condition is within the same distribution as the first. The limits are calculated using a bootstrap technique to correct for any bias in the estimated threshold. Power changes between the two conditions are considered significantly different if the difference of the pseudo-t value is larger than expected within conditions. To demonstrate the effectiveness of the technique, the proposed method was applied to MEG responses to two distinct visual stimuli recorded from a single subject. Major differences of brain activity between the two conditions were found in the occipital region. These results were validated using four pairs of split-half datasets, generated from either the odd or even trials in each condition. The method of split-half resampling should therefore be useful for localizing significant differences in brain activity between conditions within individual subjects.