The main goal of this paper is to employ longitudinal trajectories in a significant number of sub-regional brain volumetric MRI data as statistical predictors for Alzheimer's disease(AD)clas-sification.We use logi...The main goal of this paper is to employ longitudinal trajectories in a significant number of sub-regional brain volumetric MRI data as statistical predictors for Alzheimer's disease(AD)clas-sification.We use logistic regression in a Bayesian framework that includes many functional predictors.The direct sampling of regression Coefficients from the Bayesian logistic model is dif-ficult due to its complicated likelihood function.In high-dimensional scenarios,the selection of predictors is paramount with the introduction of either spike-and-slab priors,non-local priors,or Horseshoe priors.We seek to avoid the complicated Metropolis-Hastings approach and to develop an easily implementable Gibbs sampler.In addition,the Bayesian estimation provides proper estimates of the model parameters,which are also useful for building inference.Another advantage of working with logistic regression is that it calculates the log of odds of relative risk for AD compared to normal control based on the selected longitudinal predictors,rather than simply classifying patients based on cross-sectional estimates.Ultimately,however,we com-bine approaches and use a probability threshold to classify individual patients.We employ 49 functional predictors consisting of volumetric estimates of brain sub-regions,chosen for their established clinical significance.Moreover,the use of spike and slab priors ensures that many redundant predictors are dropped from the model.展开更多
基金This work was supported by Directorate for Mathematical and Physical Sciences[1924724].
文摘The main goal of this paper is to employ longitudinal trajectories in a significant number of sub-regional brain volumetric MRI data as statistical predictors for Alzheimer's disease(AD)clas-sification.We use logistic regression in a Bayesian framework that includes many functional predictors.The direct sampling of regression Coefficients from the Bayesian logistic model is dif-ficult due to its complicated likelihood function.In high-dimensional scenarios,the selection of predictors is paramount with the introduction of either spike-and-slab priors,non-local priors,or Horseshoe priors.We seek to avoid the complicated Metropolis-Hastings approach and to develop an easily implementable Gibbs sampler.In addition,the Bayesian estimation provides proper estimates of the model parameters,which are also useful for building inference.Another advantage of working with logistic regression is that it calculates the log of odds of relative risk for AD compared to normal control based on the selected longitudinal predictors,rather than simply classifying patients based on cross-sectional estimates.Ultimately,however,we com-bine approaches and use a probability threshold to classify individual patients.We employ 49 functional predictors consisting of volumetric estimates of brain sub-regions,chosen for their established clinical significance.Moreover,the use of spike and slab priors ensures that many redundant predictors are dropped from the model.
