Estimation of the Number of “True” Null Hypotheses in Multivariate Analysis of Neuroimaging Data

Authors: Turkheimer F.E.^{1, 2}; Smith C.B.¹; Schmidt K.¹

Source: NeuroImage, Volume 13, Number 5, May 2001 , pp. 920-930(11)

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Abstract:

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. Copyright 2001 Academic Press.

Keywords: PET; autoradiography; multiple comparisons; P plot

Language: English

Document Type: Research article

Affiliations: 1: Laboratory of Cerebral Metabolism, National Institute of Mental Health, Bethesda, Maryland, 20892 2: MRC Cyclotron Unit, Hammersmith Hospital, DuCane Road, London, W12-0NN, United Kingdom

Publication date: 2001-05-01