Send to

Choose Destination
Neuroimage. 2001 May;13(5):920-30.

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

Author information

Laboratory of Cerebral Metabolism, National Institute of Mental Health, Bethesda, Maryland 20892, USA.


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 statistical 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 specified level. An initial assumption of these methods is a "global null hypothesis," i.e., the statistics computed on each site are assumed to be generated by null distributions. This framework may be too conservative when a significant proportion of the sites is affected by the experimental manipulation. This report presents the development of a rigorous statistical procedure for use with a previously reported graphical method, the P plot, for estimation of the number of "true" null hypotheses in the set. This estimate can then be used to sharpen existing multiple comparison procedures. Performance of the P plot method in the multiple comparison problem is investigated in simulation studies and in the analysis of autoradiographic data.

[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center