Recent evidence has shown that structural magnetic resonance imaging (MRI) is an effective tool for Alzheimer’s disease (AD) prediction. While traditional MRI-based prediction uses images acquired at a single time point, a longitudinal study is more sensitive and accurate in detecting early pathological changes of the AD. Two main statistical difficulties arise in the longitudinal MRI-based analysis: (i) the inconsistent longitudinal scans among subjects (i.e., the different scanning time and the different total number of scans); (ii) the heterogeneous progressions of high-dimensional regions of interest (ROIs) in MRI. In this work, we propose a new feature selection and estimation method which can be applied to extract AD-related features from the heterogeneous longitudinal MRI. A key ingredient of our approach is a hybrid of the smoothing splines and the l₁-penalty. Smoothing splines can integrate information from heterogeneous progressions of ROIs and adapt to inconsistent scans of MRIs. The selection property of the l₁-penalty helps to select important ROIs related to AD. We introduce an efficient algorithm to perform the proposed method. Real data experiments on the Alzheimer’s Disease Neuroimaging Initiative database are provided to corroborate some advantages of the proposed method for AD prediction in longitudinal studies.

最近的证据表明，结构磁共振成像（MRI）是阿尔茨海默病（AD）预测的有效工具。传统的基于MRI的预测使用的是在单个时间点获取的图像，而纵向研究在检测AD的早期病理变化方面更为灵敏和准确。在基于MRI的纵向分析中出现了两个主要的统计困难：（i）受试者之间的纵向扫描不一致（即，不同的扫描时间和不同的扫描总数）；（ii）MRI中高维关注区域（ROI）的异类进展。在这项工作中，我们提出了一种新的特征选择和估计方法，该方法可用于从异构纵向MRI中提取与AD相关的特征。l _1-罚款。平滑样条可以整合来自ROI异类进展的信息，并适应MRI的不一致扫描。l ₁惩罚的选择属性有助于选择与AD相关的重要ROI。我们介绍了一种有效的算法来执行所提出的方法。提供了阿尔茨海默氏病神经影像计划数据库上的实际数据实验，以证实纵向研究中提出的AD预测方法的某些优势。