The spike-and-slab elastic net as a classification tool in Alzheimer's disease

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dc.contributor.authorAlzheimer's Dis Neuroimaging Intia
dc.contributor.authorLeach, Justin M.
dc.contributor.authorEdwards, Lloyd J.
dc.contributor.authorKana, Rajesh
dc.contributor.authorVisscher, Kristina
dc.contributor.authorYi, Nengjun
dc.contributor.authorAban, Inmaculada
dc.contributor.otherUniversity of Alabama Birmingham
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2023-09-28T20:43:32Z
dc.date.available2023-09-28T20:43:32Z
dc.date.issued2022
dc.description.abstractAlzheimer's disease (AD) is the leading cause of dementia and has received considerable research attention, including using neuroimaging biomarkers to classify patients and/or predict disease progression. Generalized linear models, e.g., logistic regression, can be used as classifiers, but since the spatial measurements are correlated and often outnumber subjects, penalized and/or Bayesian models will be identifiable, while classical models often will not. Many useful models, e.g., the elastic net and spike-and-slab lasso, perform automatic variable selection, which removes extraneous predictors and reduces model variance, but neither model exploits spatial information in selecting variables. Spatial information can be incorporated into variable selection by placing intrinsic autoregressive priors on the logit probabilities of inclusion within a spike-and-slab elastic net framework. We demonstrate the ability of this framework to improve classification performance by using cortical thickness and tau-PET images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to classify subjects as cognitively normal or having dementia, and by using a simulation study to examine model performance using finer resolution images.en_US
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.citationLeach, J. M., Edwards, L. J., Kana, R., Visscher, K., Yi, N., & Aban, I. (2022). The spike-and-slab elastic net as a classification tool in Alzheimer’s disease. In X. Song (Ed.), PLOS ONE (Vol. 17, Issue 2, p. e0262367). Public Library of Science (PLoS). https://doi.org/10.1371/journal.pone.0262367
dc.identifier.doi10.1371/journal.pone.0262367
dc.identifier.orcidhttps://orcid.org/0000-0003-0167-9097
dc.identifier.orcidhttps://orcid.org/0000-0002-7788-4575
dc.identifier.urihttps://ir.ua.edu/handle/123456789/11794
dc.languageEnglish
dc.language.isoen_US
dc.publisherPLOS
dc.rights.licenseAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBAYESIAN VARIABLE SELECTION
dc.subjectGENERALIZED LINEAR-MODELS
dc.subjectSURFACE-BASED ANALYSIS
dc.subjectHUMAN CEREBRAL-CORTEX
dc.subjectHYPOTHETICAL MODEL
dc.subjectMILD
dc.subjectPART
dc.subjectMRI
dc.subjectMultidisciplinary Sciences
dc.titleThe spike-and-slab elastic net as a classification tool in Alzheimer's diseaseen_US
dc.typeArticle
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