Multiple Testing for Neuroimaging via Hidden Markov Random Field

• Activity Number: 354
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Hai Shu*+ and Bin Nan and Robert Koeppe
• Companies: University of Michigan and University of Michigan and University of Michigan
• Keywords: Alzheimer's disease ; False discovery rate ; Generalized expectation-maximization algorithm ; Ising model ; Local significance index ; Mild cognitive impairment

Abstract

Traditional voxel-level multiple testing procedures in neuroimaging, which are 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, 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 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 Alzheimer's Disease Neuroimaging Initiative's 18F-Fluorodeoxyglucose positron emission tomography imaging study.