Group maps created from individual functional maps provide useful summaries of patterns of brain activation. Different methods for combining information have been proposed in the statistical literature and have been recently applied to fMRI data. Since these group maps are statistics, it is natural to ask how robust they are, that is, are they sensitive to the effects of unusual subjects? "Unusual" might be in terms of extent, location, or strength of activation. Our approach in this paper is to jackknife group maps formed by different combining procedures; the jackknife method, which involves deleting each observation (subject) in turn and recalculating the statistic (the group map), is commonly used for the purpose of assessing sensitivity. We examine the theoretical properties of four combining methods. In addition, via a collection of measures defined on the difference between group maps based on the entire sample and based on the jackknifed samples, we evaluate the robustness of these same methods on data from an fMRI experiment. Results indicate that there is a type of tradeoff in the combining techniques we consider, between robustness and conservativeness: methods that are liberal, in that they allow for the discovery of many active voxels, tend also to be more sensitive to the influences of individual subjects.
All Science Journal Classification (ASJC) codes
- Cognitive Neuroscience