Multiple testing for neuroimaging via hidden Markov random field

Hai Shu, Bin Nan, Robert Koeppe

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)741-750
Number of pages10
Issue number3
StatePublished - Sep 1 2015


  • Alzheimer's disease
  • False discovery rate
  • Generalized expectation-maximization algorithm
  • Ising model
  • Local significance index
  • Penalized likelihood

ASJC Scopus subject areas

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


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