Predicting Brain Age of Healthy Adults Based on Structural MRI Parcellation Using Convolutional Neural Networks
Predicting Brain Age of Healthy Adults Based on Structural MRI Parcellation Using Convolutional Neural Networks
复制标题
使用卷积神经网络根据结构 MRI 分区预测健康成年人的大脑年龄
DOI：
10.3389/fneur.2019.01346
复制
发表时间：
2020-01-08
影响因子：
3.4
通讯作者：
Guo, Xiaojuan
中科院分区：
文献类型：
--
作者：
Jiang, Huiting;Lu, Na;Guo, Xiaojuan
结构磁共振成像（MRI）研究表明，在正常发育和衰老过程中，大脑不仅在区域上而且在网络水平上经历与年龄相关的神经解剖学变化。近年来，许多研究都集中在使用结构MRI测量来估计年龄。然而，年龄预测对不同结构网络的影响尚不清楚。在这项研究中，我们使用卷积神经网络（CNN）建立了基于常见结构网络的年龄预测模型，数据来自1，454名18-90岁的健康受试者。首先，基于CorticalParcellation_Yeo2011的参考图，我们获得了每个受试者的结构网络图像，包括以下图像：额顶网络（FPN），背侧注意网络（DAN），默认模式网络（DMN），躯体运动网络（SMN），腹侧注意网络（货车），视觉网络（VN）和边缘网络（LN）。然后，我们使用大型训练数据集（n = 1，303）和测试数据集中受试者的预测年龄（n = 151）为每个结构网络构建了3D CNN模型。最后，我们将CNN的年龄预测性能与高斯过程回归（GPR）和相关向量回归（RVR）进行了比较。CNN的结果表明，FPN，DAN和DMN表现出最佳的年龄预测精度，平均绝对误差（MAE）分别为5.55年，5.77年和6.07年，其他四个网络，即，SMN、货车、VN和LN倾向于具有大于8年的较大MAE。对于GPR和RVR，前三名的预测精度仍然来自FPN，DAN和DMN;此外，CNN对这三个网络的预测精度高于GPR和RVR。我们的研究结果表明，CNN具有最佳的年龄预测性能，我们的年龄预测模型可以根据年龄预测差异用于脑疾病诊断。
Structural magnetic resonance imaging (MRI) studies have demonstrated that the brain undergoes age-related neuroanatomical changes not only regionally but also on the network level during the normal development and aging process. In recent years, many studies have focused on estimating age using structural MRI measurements. However, the age prediction effects on different structural networks remain unclear. In this study, we established age prediction models based on common structural networks using convolutional neural networks (CNN) with data from 1,454 healthy subjects aged 18–90 years. First, based on the reference map of CorticalParcellation_Yeo2011, we obtained structural network images for each subject, including images of the following: the frontoparietal network (FPN), the dorsal attention network (DAN), the default mode network (DMN), the somatomotor network (SMN), the ventral attention network (VAN), the visual network (VN), and the limbic network (LN). Then, we built a 3D CNN model for each structural network using a large training dataset (n = 1,303) and the predicted ages of the subjects in the test dataset (n = 151). Finally, we estimated the age prediction performance of CNN compared with Gaussian process regression (GPR) and relevance vector regression (RVR). The results of CNN showed that the FPN, DAN, and DMN exhibited the optimal age prediction accuracies with mean absolute errors (MAEs) of 5.55 years, 5.77 years, and 6.07 years, respectively, and the other four networks, i.e., the SMN, VAN, VN, and LN, tended to have larger MAEs of more than 8 years. With respect to GPR and RVR, the top three prediction accuracies were still from the FPN, DAN, and DMN; moreover, CNN made more precise predictions than GPR and RVR for these three networks. Our findings suggested that CNN has the optimal age prediction performance, and our age prediction model can be potentially used for brain disorder diagnosis according to age prediction differences.