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 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 speci...

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Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Fla.), 2001-05, Vol.13 (5), p.920-930
Main Authors: Turkheimer, F.E., Smith, C.B., Schmidt, K.
Format: Article
Language:English
Subjects:
Anesthesia, General
Animals
Autoradiography
Blood Glucose - metabolism
Brain - diagnostic imaging
Brain - drug effects
Brain - physiology
Brain Mapping
Computer Graphics
Computer Simulation
Diazepam - pharmacology
Energy Metabolism - drug effects
Energy Metabolism - physiology
Humans
Hypotheses
Image processing
Image Processing, Computer-Assisted
Ketamine - pharmacology
Mathematical Computing
Monte Carlo Method
multiple comparisons
Multivariate analysis
Neuroimaging
P plot
PET
Rats
Statistical analysis
Statistics
Tomography, Emission-Computed
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Summary: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.
ISSN:1053-8119
1095-9572
DOI:10.1006/nimg.2001.0764