{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multiple-testing-for-neuroimaging-via-hidden","title":"Multiple Testing for Neuroimaging via Hidden Markov Random Field","arxiv_id":"1404.1371","date":"2014-04-04","proceeding":null,"authors":["Hai Shu","Bin Nan","Robert Koeppe"],"abstract":"Traditional voxel-level multiple testing procedures in neuroimaging, mostly\n$p$-value based, often ignore the spatial correlations among neighboring voxels\nand thus suffer from substantial loss of power. We extend the\nlocal-significance-index based procedure originally developed for the hidden\nMarkov chain models, which aims to minimize the false nondiscovery rate subject\nto a constraint on the false discovery rate, to three-dimensional neuroimaging\ndata using a hidden Markov random field model. A generalized\nexpectation-maximization algorithm for maximizing the penalized likelihood is\nproposed for estimating the model parameters. Extensive simulations show that\nthe proposed approach is more powerful than conventional false discovery rate\nprocedures. We apply the method to the comparison between mild cognitive\nimpairment, a disease status with increased risk of developing Alzheimer's or\nanother dementia, and normal controls in the FDG-PET imaging study of the\nAlzheimer's Disease Neuroimaging Initiative.","url_abs":"http://arxiv.org/abs/1404.1371v2","url_pdf":"http://arxiv.org/pdf/1404.1371v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multiple-testing-for-neuroimaging-via-hidden","repo_url":"https://github.com/shu-hai/FDRhmrf","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}