Multiple testing for neuroimaging via hidden Markov random field. Issue 3 (26th May 2015)

• Record Type: Journal Article
• Authors: Shu, Hai; Nan, Bin; Koeppe, Robert
• Is Part Of: Biometrics. Volume 71:Issue 3(2015)
• Page Start: 741
• Page End: 750
• Publication Date: 2015-05-26
• Subjects: Alzheimer's disease -- False discovery rate -- Generalized expectation–maximization algorithm -- Ising model -- Local significance index -- Penalized likelihood; Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.12329
• Languages: English
• ISSNs: 0006-341X

Abstract

Summary: Traditional voxel‐level multiple testing procedures in neuroimaging, mostly p ‐value based, often ignore the spatial correlations among neighboring voxels and thus suffer from substantial loss of power. We extend the local‐significance‐index based procedure originally developed for the hidden Markov chain models, which aims to minimize the false nondiscovery rate subject to a constraint on the false discovery rate, to three‐dimensional neuroimaging data using a hidden Markov random field model. A generalized expectation–maximization algorithm for maximizing the penalized likelihood is proposed for estimating the model parameters. Extensive simulations show that the proposed approach is more powerful than conventional false discovery rate procedures. We apply the method to the comparison between mild cognitive impairment, a disease status with increased risk of developing Alzheimer's or another dementia, and normal controls in the FDG‐PET imaging study of the Alzheimer's Disease Neuroimaging Initiative.