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 Markov...
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| Published in | Biometrics Vol. 71; no. 3; pp. 741 - 750 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Blackwell Publishing Ltd
01.09.2015
International Biometric Society |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0006-341X 1541-0420 |
| DOI | 10.1111/biom.12329 |
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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. |
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| 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. 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. 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. 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. |
| Author | Shu, Hai Nan, Bin Koeppe, Robert |
| AuthorAffiliation | 2 Department of Radiology, University of Michigan, Ann Arbor, Michigan, U.S.A 1 Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A |
| AuthorAffiliation_xml | – name: 2 Department of Radiology, University of Michigan, Ann Arbor, Michigan, U.S.A – name: 1 Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A |
| Author_xml | – sequence: 1 givenname: Hai surname: Shu fullname: Shu, Hai organization: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A – sequence: 2 givenname: Bin surname: Nan fullname: Nan, Bin email: bnan@umich.edu organization: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A – sequence: 3 givenname: Robert surname: Koeppe fullname: Koeppe, Robert organization: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109, U.S.A |
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| Keywords | False discovery rate Ising model Local significance index Alzheimer's disease Penalized likelihood Generalized expectation-maximization algorithm |
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| References_xml | – reference: Hoff, P. D. (2009). A First Course in Bayesian Statistical Methods. New York: Springer. – reference: Welch, B. L. (1947). The generalization of 'Student's' problem when several different population variances are involved. Biometrika 34, 28-35. – reference: Storey, J. D. (2003). The positive false discovery rate: A Bayesian interpretation and the q-value. The Annals of Statistics 31, 2013-2035. – reference: Booth, J. G. and Hobert, J. P. (1999). Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm. Journal of the Royal Statistical Society, Series B 61, 265-285. – reference: Magder, L. S. and Zeger, S. L., (1996). A smooth nonparametric estimate of a mixing distribution using mixtures of Gaussians. Journal of the American Statistical Association 91, 1141-1151. – reference: Stoer, J. and Bulirsch, R. (2002). Introduction to Numerical Analysis, 3rd edition. New York: Springer. – reference: Garey, L. J. (2006). 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| Snippet | Traditional voxel-level multiple testing procedures in neuroimaging, mostly p-value based, often ignore the spatial correlations among neighboring voxels and... Summary Traditional voxel‐level multiple testing procedures in neuroimaging, mostly p‐value based, often ignore the spatial correlations among neighboring... Summary Traditional voxel-level multiple testing procedures in neuroimaging, mostly p-value based, often ignore the spatial correlations among neighboring... Traditional voxel‐level multiple testing procedures in neuroimaging, mostly p‐value based, often ignore the spatial correlations among neighboring voxels and... Traditional voxel-level multiple testing procedures in neuroimaging, mostly p -value based, often ignore the spatial correlations among neighboring voxels and... |
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| SubjectTerms | Algorithms Alzheimer disease Alzheimer Disease - diagnostic imaging Alzheimer's disease BIOMETRIC METHODOLOGY biometry Cognitive Dysfunction - diagnostic imaging Computer Simulation False discovery rate Fluorodeoxyglucose F18 Generalized expectation-maximization algorithm Humans image analysis Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Ising model Local significance index Markov analysis Markov chain Markov Chains Medical imaging Models, Statistical Neuroimaging - methods Penalized likelihood Positron-Emission Tomography - methods Radiopharmaceuticals Reproducibility of Results risk Sensitivity and Specificity |
| Title | Multiple testing for neuroimaging via hidden Markov random field |
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