Wang Jinfang, Zhao Cui, Wei Jing, Li Chunlin, Zhang Xu, Liang Ying, Zhang Yumei
Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, China National Clinical Research Center for Neurological Diseases, Center of Stroke, Beijing Institute for Brain Disorders, Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China.
Department of Neurology, General Hospital of The Yang Tze River Shipping, Wuhan Brain Hospital, Wuhan, China.
Ann Transl Med. 2022 Mar;10(5):246. doi: 10.21037/atm-21-3571.
Vascular risk factors like white matter lesions (WMLs) are increasingly recognized as risk factors for vascular dementia (VaD) and can predict Alzheimer's disease (AD) at least a decade before the clinical stage of the disease. This study aimed to predict cognitive decline and use machine learning techniques to classify older individuals (aged 50 years or older) with WMLs as having vascular mild cognitive impairment (VaMCI), VaD, or in good cognitive health (CH).
A total of 79 individuals with WMLs were selected for this study and categorized into the following 3 groups: CH (n=25), VaMCI (n=33), and VaD (n=21). Data from the entire cohort was then divided into a training dataset (n=56) and testing dataset (n=23). The data were extracted from gray matter (GM) segmentations using voxel-based morphometry (VBM). A relevance vector regression (RVR) approach was used to test the relationship between the structural brain images and clinical scores. To predict the individual-level subtypes, we applied 2 different machine learning-based classifiers: support vector machine (SVM) and Gaussian process classification (GPC). All predictive models were trained on the training dataset and then validated on the testing dataset of age-matched participants.
Multi-domain cognitive performance could be predicted based on the pattern of GM atrophy in older people with WMLs using a RVR approach. The classification of VaD versus CH (cross-validation accuracy =93.94%, test set accuracy =76.92%) and VaMCI versus CH (cross-validation accuracy =95.24%, test set accuracy =87.50%) could be successfully achieved using both SVM and GPC. However, SVM (cross-validation accuracy =67.57%, test set accuracy =70.59%) performed better than GPC in the classification of VaD versus VaMCI.
Based on the patterns of gray matter and RVR-based model could achieve prediction of cognitive test scores, and SVM and GPC could classify the severity of cognitive impairment in older people with WMLs.
诸如脑白质病变（WMLs）等血管危险因素日益被视为血管性痴呆（VaD）的危险因素，并且至少在疾病临床阶段的十年前就能预测阿尔茨海默病（AD）。本研究旨在预测认知功能衰退，并使用机器学习技术对患有WMLs的老年人（年龄50岁及以上）进行分类，确定其患有血管性轻度认知障碍（VaMCI）、VaD还是认知健康状况良好（CH）。
本研究共选取了79名患有WMLs的个体，并将其分为以下3组：认知健康组（n = 25）、VaMCI组（n = 33）和VaD组（n = 21）。然后将整个队列的数据分为训练数据集（n = 56）和测试数据集（n = 23）。使用基于体素的形态学测量（VBM）从灰质（GM）分割中提取数据。采用相关向量回归（RVR）方法来测试脑结构图像与临床评分之间的关系。为了预测个体水平的亚型，我们应用了两种不同的基于机器学习的分类器：支持向量机（SVM）和高斯过程分类（GPC）。所有预测模型均在训练数据集上进行训练，然后在年龄匹配参与者的测试数据集上进行验证。
使用RVR方法，基于患有WMLs的老年人的GM萎缩模式可以预测多领域认知表现。使用SVM和GPC均能成功实现VaD与CH的分类（交叉验证准确率 = 93.94%，测试集准确率 = 76.92%）以及VaMCI与CH的分类（交叉验证准确率 = 95.24%，测试集准确率 = 87.50%）。然而，在VaD与VaMCI的分类中，SVM（交叉验证准确率 = 67.57%，测试集准确率 = 70.59%）的表现优于GPC。
基于灰质模式和基于RVR的模型可以实现对认知测试分数的预测，并且SVM和GPC可以对患有WMLs的老年人的认知障碍严重程度进行分类。
