Dept. of Mathematics, Koç University, 34450 Sariyer, Istanbul, Turkey.
Curr Alzheimer Res. 2012 Oct;9(8):972-81. doi: 10.2174/156720512803251138.
In this article, we use longitudinal morphometry (shape and size) measures of hippocampus in subjects with mild dementia of Alzheimer type (DAT) and nondemented controls in logistic discrimination. The morphometric measures we use are volume and metric distance measures at baseline and follow-up (two years apart from baseline). Morphometric differences with respect to a template hippocampus were measured by the metric distance obtained from the large deformation diffeomorphic metric mapping (LDDMM) algorithm. LDDMM assigns metric distances on the space of anatomical images, thereby allowing for the direct comparison and quantization of morphometric changes. We also apply principal component analysis (PCA) on volume and metric distance measures to obtain principal components that capture some salient aspect of morphometry. We construct classifiers based on logistic regression to distinguish diseased and healthy hippocampi (hence potentially diagnose the mild form of DAT). We consider logistic classifiers based on volume and metric distance change over time (from baseline to follow-up), on the raw volumes and metric distances, and on principal components from various types of PCA analysis. We provide a detailed comparison of the performance of these classifiers and guidelines for their practical use. Moreover, combining the information conveyed by volume and metric distance measures by PCA can provide a better biomarker for detection of dementia compared to volume, metric distance, or both.
在本文中，我们使用纵向形态计量学（形状和大小）测量轻度阿尔茨海默病型痴呆（DAT）患者和非痴呆对照者的海马体。我们使用的形态计量学测量值是基线和随访（与基线相隔两年）时的体积和度量距离测量值。通过大变形微分同胚度量映射（LDDMM）算法获得的度量距离来测量相对于模板海马体的形态计量差异。LDDMM 在解剖图像空间中分配度量距离，从而允许直接比较和量化形态计量变化。我们还对体积和度量距离测量值应用主成分分析（PCA），以获得捕获形态计量某些显著方面的主成分。我们基于逻辑回归构建分类器，以区分患病和健康的海马体（因此可以潜在地诊断出轻度 DAT 形式）。我们考虑基于体积和度量距离随时间变化（从基线到随访）的逻辑分类器、原始体积和度量距离以及来自各种 PCA 分析类型的主成分的逻辑分类器。我们详细比较了这些分类器的性能，并为其实际应用提供了指导。此外，通过 PCA 将体积和度量距离测量值所传达的信息相结合，可以比体积、度量距离或两者都更有效地提供用于检测痴呆的生物标志物。
