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Multiple testing for neuroimaging via hidden Markov random field

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  • Hai Shu
  • Bin Nan
  • Robert Koeppe

Abstract

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.

Suggested Citation

  • Hai Shu & Bin Nan & Robert Koeppe, 2015. "Multiple testing for neuroimaging via hidden Markov random field," Biometrics, The International Biometric Society, vol. 71(3), pages 741-750, September.
    • Handle: RePEc:bla:biomet:v:71:y:2015:i:3:p:741-750
    DOI: 10.1111/biom.12329
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    References listed on IDEAS

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    Cited by:

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    2. Joungyoun Kim & Donghyeon Yu & Johan Lim & Joong-Ho Won, 2018. "A peeling algorithm for multiple testing on a random field," Computational Statistics, Springer, vol. 33(1), pages 503-525, March.
    3. Pengfei Wang & Wensheng Zhu, 2022. "Large‐scale covariate‐assisted two‐sample inference under dependence," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1421-1447, December.
    4. Tingting Cui & Pengfei Wang & Wensheng Zhu, 2021. "Covariate-adjusted multiple testing in genome-wide association studies via factorial hidden Markov models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 737-757, September.
    5. Wang, Xia & Shojaie, Ali & Zou, Jian, 2019. "Bayesian hidden Markov models for dependent large-scale multiple testing," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 123-136.

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