Liu Xiang, Shu Yongqiang, Yu Pengfei, Li Haijun, Duan Wenfeng, Wei Zhipeng, Li Kunyao, Xie Wei, Zeng Yaping, Peng Dechang
Department of Radiology, the First Affiliated Hospital of Nanchang University, Jiangxi, China.
Big Data Center, the Second Affiliated Hospital of Nanchang University, Jiangxi, China.
Front Neurol. 2022 Aug 25;13:1005650. doi: 10.3389/fneur.2022.1005650. eCollection 2022.
In this study, we aimed to use voxel-level degree centrality (DC) features in combination with machine learning methods to distinguish obstructive sleep apnea (OSA) patients with and without mild cognitive impairment (MCI). Ninety-nine OSA patients were recruited for rs-MRI scanning, including 51 MCI patients and 48 participants with no mild cognitive impairment. Based on the Automated Anatomical Labeling (AAL) brain atlas, the DC features of all participants were calculated and extracted. Ten DC features were screened out by deleting variables with high pin-correlation and minimum absolute contraction and performing selective operator lasso regression. Finally, three machine learning methods were used to establish classification models. The support vector machine method had the best classification efficiency (AUC = 0.78), followed by random forest (AUC = 0.71) and logistic regression (AUC = 0.77). These findings demonstrate an effective machine learning approach for differentiating OSA patients with and without MCI and provide potential neuroimaging evidence for cognitive impairment caused by OSA.
在本研究中，我们旨在结合体素水平的度中心性（DC）特征与机器学习方法，以区分有和没有轻度认知障碍（MCI）的阻塞性睡眠呼吸暂停（OSA）患者。招募了99名OSA患者进行静息态功能磁共振成像（rs-MRI）扫描，其中包括51名MCI患者和48名无轻度认知障碍的参与者。基于自动解剖标记（AAL）脑图谱，计算并提取了所有参与者的DC特征。通过删除高pin相关性和最小绝对收缩的变量并执行选择性算子套索回归，筛选出10个DC特征。最后，使用三种机器学习方法建立分类模型。支持向量机方法具有最佳的分类效率（曲线下面积[AUC]=0.78），其次是随机森林（AUC=0.71）和逻辑回归（AUC=0.77）。这些发现证明了一种有效的机器学习方法，可用于区分有和没有MCI的OSA患者，并为OSA引起的认知障碍提供潜在的神经影像学证据。
