Longitudinal structural MRI analysis and classification in Alzheimer's disease and mild cognitive impairment
Longitudinal structural MRI analysis and classification in Alzheimer's disease and mild cognitive impairment
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阿尔茨海默病和轻度认知障碍的纵向结构MRI分析和分类
DOI：
10.1002/ima.22390
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发表时间：
2020
影响因子：
3.3
通讯作者：
Xiaoli Yu
中科院分区：
文献类型：
--
作者：
Yingteng Zhang;Shenquan Liu;Xiaoli Yu
阿尔茨海默病（AD）和轻度认知障碍（MCI）的早期诊断或检测是至关重要的，以便提前干预并更好地了解神经退行性过程。灰质体积（GMV）在显示脑区独特的解剖特征和进一步区分AD、MCI和正常对照（NC）方面起着重要作用。在这项研究中，317名受试者（100名NC，58名稳定MCI（sMCI），53名转化MCI（cMCI）和106名AD）选自阿尔茨海默病神经影像学倡议数据库。首先，比较不同时间点组间比较的GMV模式的差异和同一组内纵向模式的发展。然后，采用支持向量机（SVM）结合嵌套留一交叉验证（LOOCV）方法，采用纵向特征组合策略构建分类模型。从NC向AD发展的过程中，脑结构经历了一个渐进的变化过程。此外，基线GMV与2年随访数据的纵向测量相结合产生了最佳分类结果。具体而言，AD-NC比较实现了最佳分类性能，准确度为98.06%，灵敏度为97.17%，特异性为99.00%，阳性预测值（PPV）为99.04%，阴性预测值（NPV）为97.06%。MCI的两种亚型（即sMCI和cMCI）的比较也达到了很高的准确性。其他组间比较也获得了较高的分类性能。据统计，尾状核、海马、颞极和豆状壳核是组间比较最重要的贡献区域。我们的研究有可能改善MCI亚型的临床诊断，并预测其转化为AD的风险。
Early diagnosis or detection of Alzheimer's disease (AD) and mild cognitive impairment (MCI) is crucial so as to intervene in advance and to better understand the neurodegenerative process. Gray matter volume (GMV) plays an important role in demonstrating unique anatomical characteristics of the brain regions and further differentiates AD, MCI and normal control (NC). In this study, 317 subjects (100 NC, 58 stable MCI (sMCI), 53 converted MCI (cMCI) and 106 AD) are selected from the Alzheimer's Disease Neuroimaging Initiative database. First, the differences of GMV patterns among the between‐group comparisons at different time points and the development of longitudinal pattern within the same group are compared. Next, the longitudinal feature combination strategy is applied to construct the classification model by using a support vector machine (SVM) combined with the nested leave‐one‐out cross‐validation (LOOCV) method. The brain structure experiences a gradual change in the process of developing from NC to AD. In addition, the baseline GMV combined with the longitudinal measurements for 2 years of follow‐up data yielded optimal classification results. Specifically, the AD‐NC comparison achieves the best classification performance with 98.06% accuracy, 97.17% sensitivity, 99.00% specificity, 99.04% positive predictive value (PPV) and 97.06% negative predictive value (NPV). The comparison of the two subtypes of MCI (ie, sMCI and cMCI) also achieves high accuracy. Other between‐group comparisons also receive high classification performance. According to statistics, caudate nucleus, hippocampus, temporal pole and lenticular putamen are the most important contribution areas to the between‐group comparisons. Our research has the potential to improve the clinical diagnosis of subtypes of MCI and predict the risk of its conversion to AD.