Li Ying, Fang Yixian, Wang Jiankun, Zhang Huaxiang, Hu Bin
Key Laboratory of TCM Data Cloud Service in Universities of Shandong, Shandong Management University, Jinan 250357, China.
School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China.
Biomed Res Int. 2021 Sep 2;2021:5531940. doi: 10.1155/2021/5531940. eCollection 2021.
Accurate recognition of progressive mild cognitive impairment (MCI) is helpful to reduce the risk of developing Alzheimer's disease (AD). However, it is still challenging to extract effective biomarkers from multivariate brain structural magnetic resonance imaging (MRI) features to accurately differentiate the progressive MCI from stable MCI. We develop novel biomarkers by combining subspace learning methods with the information of AD as well as normal control (NC) subjects for the prediction of MCI conversion using multivariate structural MRI data. Specifically, we first learn two projection matrices to map multivariate structural MRI data into a common label subspace for AD and NC subjects, where the original data structure and the one-to-one correspondence between multiple variables are kept as much as possible. Afterwards, the multivariate structural MRI features of MCI subjects are mapped into a common subspace according to the projection matrices. We then perform the self-weighted operation and weighted fusion on the features in common subspace to extract the novel biomarkers for MCI subjects. The proposed biomarkers are tested on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results indicate that our proposed biomarkers outperform the competing biomarkers on the discrimination between progressive MCI and stable MCI. And the improvement from the proposed biomarkers is not limited to a particular classifier. Moreover, the results also confirm that the information of AD and NC subjects is conducive to predicting conversion from MCI to AD. In conclusion, we find a good representation of brain features from high-dimensional MRI data, which exhibits promising performance for predicting conversion from MCI to AD.
准确识别进展性轻度认知障碍（MCI）有助于降低患阿尔茨海默病（AD）的风险。然而，从多变量脑结构磁共振成像（MRI）特征中提取有效的生物标志物以准确区分进展性MCI和稳定型MCI仍然具有挑战性。我们通过将子空间学习方法与AD以及正常对照（NC）受试者的信息相结合，开发新的生物标志物，以使用多变量结构MRI数据预测MCI的转化。具体而言，我们首先学习两个投影矩阵，将多变量结构MRI数据映射到AD和NC受试者的公共标签子空间中，在该子空间中尽可能保留原始数据结构以及多个变量之间的一一对应关系。之后，根据投影矩阵将MCI受试者的多变量结构MRI特征映射到公共子空间中。然后，我们对公共子空间中的特征进行自加权操作和加权融合，以提取MCI受试者的新生物标志物。所提出的生物标志物在阿尔茨海默病神经影像倡议（ADNI）数据集上进行了测试。实验结果表明，我们提出的生物标志物在区分进展性MCI和稳定型MCI方面优于竞争生物标志物。并且所提出的生物标志物带来的改进不限于特定的分类器。此外，结果还证实AD和NC受试者的信息有助于预测从MCI到AD的转化。总之，我们从高维MRI数据中找到了脑特征的良好表示，其在预测从MCI到AD的转化方面表现出有前景的性能。
