Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer's disease.
Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer's disease.
复制标题
预测阿尔茨海默病的纵向测量和层次分类框架
DOI：
10.1038/srep39880
复制
发表时间：
2017-01-12
影响因子：
4.6
通讯作者：
Alzheimer’s Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Huang M;Yang W;Feng Q;Chen W;Alzheimer’s Disease Neuroimaging Initiative
对阿尔茨海默病(AD)的准确预测对于该疾病的早期诊断和治疗具有重要意义。轻度认知障碍(MCI)是AD的早期阶段。因此，对于完全发展为AD的高危MCI患者应予以识别，以准确预测AD。然而，由于神经成像数据的复杂特性，脑图像与AD之间的关系很难构建。为了解决这一问题，我们提出了一种MCI脑图像的纵向测量方法和一种用于AD预测的分层分类方法。对MCI患者的纵向图像进行了研究，以获得关于MCI纵向变化的重要信息，这些信息可用于将MCI受试者分为MCI转换(MCIC)和MCI非转换(MCInc)个体。此外，在分类器中引入了一个层次框架来管理高维问题，并结合空间信息来提高预测精度。对131例MCI患者(70例MCIC和61例MCINC)在不同时间点的MRI扫描进行了评估。结果表明，该方法对MCIC和MCInc.的分类准确率达到了79.4%，对AD的预测表现出很好的性能。
Accurate prediction of Alzheimer’s disease (AD) is important for the early diagnosis and treatment of this condition. Mild cognitive impairment (MCI) is an early stage of AD. Therefore, patients with MCI who are at high risk of fully developing AD should be identified to accurately predict AD. However, the relationship between brain images and AD is difficult to construct because of the complex characteristics of neuroimaging data. To address this problem, we present a longitudinal measurement of MCI brain images and a hierarchical classification method for AD prediction. Longitudinal images obtained from individuals with MCI were investigated to acquire important information on the longitudinal changes, which can be used to classify MCI subjects as either MCI conversion (MCIc) or MCI non-conversion (MCInc) individuals. Moreover, a hierarchical framework was introduced to the classifier to manage high feature dimensionality issues and incorporate spatial information for improving the prediction accuracy. The proposed method was evaluated using 131 patients with MCI (70 MCIc and 61 MCInc) based on MRI scans taken at different time points. Results showed that the proposed method achieved 79.4% accuracy for the classification of MCIc versus MCInc, thereby demonstrating very promising performance for AD prediction.