Multiple Testing for Neuroimaging via Hidden Markov Random Field

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 Mar...

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Vydáno v:arXiv.org
Hlavní autoři: Shu, Hai, Bin Nan, Koeppe, Robert
Médium: Paper
Jazyk:angličtina
Vydáno: Ithaca Cornell University Library, arXiv.org 28.07.2016
Témata:
Algorithms
Alzheimer's disease
Computer simulation
Disease control
Fields (mathematics)
Markov analysis
Markov chains
Maximization
Medical imaging
Neurology
Optimization
Parameter estimation
Positron emission
Test procedures
Three dimensional models
Tomography
ISSN:2331-8422
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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.
AbstractList 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.
Author Shu, Hai
Koeppe, Robert
Bin Nan
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Alzheimer's disease
Computer simulation
Disease control
Fields (mathematics)
Markov analysis
Markov chains
Maximization
Medical imaging
Neurology
Optimization
Parameter estimation
Positron emission
Test procedures
Three dimensional models
Tomography
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