Identification of Early-Stage Alzheimer's Disease Using Sulcal Morphology and Other Common Neuroimaging Indices.
Identification of Early-Stage Alzheimer's Disease Using Sulcal Morphology and Other Common Neuroimaging Indices.
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使用脑沟形态学和其他常见神经影像指标识别早期阿尔茨海默病
DOI：
10.1371/journal.pone.0170875
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Wen W
中科院分区：
文献类型：
--
作者：
Cai K;Xu H;Guan H;Zhu W;Jiang J;Cui Y;Zhang J;Liu T;Wen W
在早期阶段识别阿尔茨海默病（AD）是AD研究中的主要兴趣。以往的研究表明，局部脑沟宽度和整体脑沟指数（g-SI）的异常是早期AD患者的特征。在这项研究中，我们调查了脑沟宽度和其他三个常见的神经影像学形态学指标（皮质厚度，皮质体积和皮质下体积），以确定早期AD。这些措施在150名参与者中进行了评估，其中包括75名正常对照（NC）和75名早期AD患者。从3D T1加权图像中提取总体脑沟指数（g-SI）和五个单独脑沟（上级额沟、顶内沟、上级颞沟、中央沟和外侧裂）的宽度。其他三个传统的神经影像形态学措施的区别性能也进行了检查。信息增益（IG）被用来选择一个子集的功能，以提供重要的信息，分离NC和早期AD的主题。根据个别措施的四种模式，即，脑沟测量、皮质厚度、皮质体积、皮质下体积以及这些单独测量的组合，应用三种类型的分类器（朴素贝叶斯、逻辑回归和支持向量机）来比较分类性能。我们观察到脑沟测量值上级于或等于用于分类的其他测量值。具体而言，g-SI和侧裂的宽度是两个最敏感的沟测量，并可能是有用的神经解剖学标记物检测早期AD。当使用相同的神经解剖学特征时，我们测试的三个分类器之间没有显著差异。
Identifying Alzheimer’s disease (AD) at its early stage is of major interest in AD research. Previous studies have suggested that abnormalities in regional sulcal width and global sulcal index (g-SI) are characteristics of patients with early-stage AD. In this study, we investigated sulcal width and three other common neuroimaging morphological measures (cortical thickness, cortical volume, and subcortical volume) to identify early-stage AD. These measures were evaluated in 150 participants, including 75 normal controls (NC) and 75 patients with early-stage AD. The global sulcal index (g-SI) and the width of five individual sulci (the superior frontal, intra-parietal, superior temporal, central, and Sylvian fissure) were extracted from 3D T1-weighted images. The discriminative performances of the other three traditional neuroimaging morphological measures were also examined. Information Gain (IG) was used to select a subset of features to provide significant information for separating NC and early-stage AD subjects. Based on the four modalities of the individual measures, i.e., sulcal measures, cortical thickness, cortical volume, subcortical volume, and combinations of these individual measures, three types of classifiers (Naïve Bayes, Logistic Regression and Support Vector Machine) were applied to compare the classification performances. We observed that sulcal measures were either superior than or equal to the other measures used for classification. Specifically, the g-SI and the width of the Sylvian fissure were two of the most sensitive sulcal measures and could be useful neuroanatomical markers for detecting early-stage AD. There were no significant differences between the three classifiers that we tested when using the same neuroanatomical features.