Zhan Ye, Chen Kewei, Wu Xia, Zhang Daoqiang, Zhang Jiacai, Yao Li, Guo Xiaojuan
College of Information Science and Technology, Beijing Normal University, Beijing, China.
Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, Arizona, USA.
J Alzheimers Dis. 2015;47(4):1057-67. doi: 10.3233/JAD-142820.
Alzheimer's disease (AD) is one of the most serious progressive neurodegenerative diseases among the elderly, therefore the identification of conversion to AD at the earlier stage has become a crucial issue. In this study, we applied multimodal support vector machine to identify the conversion from normal elderly cognition to mild cognitive impairment (MCI) or AD based on magnetic resonance imaging and positron emission tomography data. The participants included two independent cohorts (Training set: 121 AD patients and 120 normal controls (NC); Testing set: 20 NC converters and 20 NC non-converters) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The multimodal results showed that the accuracy, sensitivity, and specificity of the classification between NC converters and NC non-converters were 67.5% , 73.33% , and 64% , respectively. Furthermore, the classification results with feature selection increased to 70% accuracy, 75% sensitivity, and 66.67% specificity. The classification results using multimodal data are markedly superior to that using a single modality when we identified the conversion from NC to MCI or AD. The model built in this study of identifying the risk of normal elderly converting to MCI or AD will be helpful in clinical diagnosis and pathological research.
阿尔茨海默病（AD）是老年人中最严重的进行性神经退行性疾病之一，因此在早期阶段识别向AD的转化已成为一个关键问题。在本研究中，我们应用多模态支持向量机，基于磁共振成像和正电子发射断层扫描数据，识别从正常老年认知向轻度认知障碍（MCI）或AD的转化。参与者包括来自阿尔茨海默病神经影像学倡议（ADNI）数据库的两个独立队列（训练集：121例AD患者和120例正常对照（NC）；测试集：20例NC转化者和20例NC非转化者）。多模态结果显示，NC转化者与NC非转化者之间分类的准确率、敏感性和特异性分别为67.5%、73.33%和64%。此外，经过特征选择后的分类结果准确率提高到70%，敏感性提高到75%，特异性提高到66.67%。当我们识别从NC向MCI或AD的转化时，使用多模态数据的分类结果明显优于使用单一模态的结果。本研究建立的识别正常老年人转化为MCI或AD风险的模型将有助于临床诊断和病理研究。
