Multiple Testing for Neuroimaging via Hidden Markov Random Field
This is an incremental improvement for neuroimaging researchers, addressing multiple testing issues in spatial data analysis.
The authors tackled the problem of low statistical power in neuroimaging due to ignored spatial correlations by extending a hidden Markov random field model to minimize false nondiscovery rates while controlling false discovery rates, showing through simulations that their approach is more powerful than conventional methods.
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.