Bayesian penalized model for classification and selection of functional predictors using longitudinal MRI data from ADNI
Bayesian penalized model for classification and selection of functional predictors using longitudinal MRI data from ADNI
复制标题
DOI：
10.1080/24754269.2022.2064611
复制
发表时间：
2022-05
影响因子：
0.5
通讯作者：
Asish Banik;T. Maiti;Andrew R. Bender
中科院分区：
文献类型：
--
作者：
Asish Banik;T. Maiti;Andrew R. Bender
摘要本文的主要目标是采用纵向轨迹在一个显着数量的分区脑体积MRI数据作为统计预测阿尔茨海默氏病（AD）分类。我们在贝叶斯框架中使用逻辑回归，其中包括许多功能预测因子。贝叶斯逻辑斯谛模型的似然函数比较复杂，直接从该模型中抽取回归系数比较困难。在高维场景中，预测因子的选择是至关重要的，无论是尖峰和板先验，非本地先验，或马蹄先验的介绍。我们试图避免复杂的大都会黑斯廷斯的方法，并开发一个易于实现的吉布斯采样器。此外，贝叶斯估计提供了模型参数的适当估计，这也有助于建立推断。使用逻辑回归的另一个优点是，它基于选定的纵向预测因子计算AD相对于正常对照的相对风险的比值，而不是简单地基于横截面估计对患者进行分类。然而，最终，我们结合联合收割机的方法，并使用概率阈值来分类个体患者。我们采用了49个功能预测组成的体积估计的大脑子区域，选择其建立的临床意义。此外，使用尖峰和厚片先验确保许多冗余的预测从模型中删除。
ABSTRACT 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) classification. 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 difficult 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 combine 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.