Estimation of the number of "true" null hypotheses in multivariate analysis of neuroimaging data

The repeated testing of a null univariate hypothesis in each of many sites (either regions of interest or voxels) is a common approach to the statisti cal analysis of brain functional images. Procedures, such as the Bonferroni , are available to maintain the Type I error of the set of tests at a speci fied level. An initial assumption of these methods is a "global null hypoth esis," i.e., the statistics computed on each site are assumed to be generat ed by null distributions. This framework may be too conservative when a sig nificant proportion of the sites is affected by the experimental manipulati on. This report presents the development of a rigorous statistical procedur e for use with a previously reported graphical method, the P plot, for esti mation of the number of "true" null hypotheses in the set. This estimate ca n then be used to sharpen existing multiple comparison procedures. Performa nce of the P plot method in the multiple comparison problem is investigated in simulation studies and in the analysis of autoradiographic data. (C) 20 01 Academic Press.