College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China.
Dthe Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA.
Med Image Anal. 2022 Oct;81:102550. doi: 10.1016/j.media.2022.102550. Epub 2022 Jul 16.
It has been proven that neuropsychiatric disorders (NDs) can be associated with both structures and functions of brain regions. Thus, data about structures and functions could be usefully combined in a comprehensive analysis. While brain structural MRI (sMRI) images contain anatomic and morphological information about NDs, functional MRI (fMRI) images carry complementary information. However, efficient extraction and fusion of sMRI and fMRI data remains challenging. In this study, we develop an enhanced multi-modal graph convolutional network (MME-GCN) in a binary classification between patients with NDs and healthy controls, based on the fusion of the structural and functional graphs of the brain region. First, based on the same brain atlas, we construct structural and functional graphs from sMRI and fMRI data, respectively. Second, we use machine learning to extract important features from the structural graph network. Third, we use these extracted features to adjust the corresponding edge weights in the functional graph network. Finally, we train a multi-layer GCN and use it in binary classification task. MME-GCN achieved 93.71% classification accuracy on the open data set provided by the Consortium for Neuropsychiatric Phenomics. In addition, we analyzed the important features selected from the structural graph and verified them in the functional graph. Using MME-GCN, we found several specific brain connections important to NDs.
已经证明，神经精神疾病（NDs）与大脑区域的结构和功能都有关联。因此，关于结构和功能的数据可以在综合分析中很好地结合起来。虽然脑结构磁共振成像（sMRI）图像包含关于 NDs 的解剖学和形态学信息，但功能磁共振成像（fMRI）图像则携带补充信息。然而，sMRI 和 fMRI 数据的有效提取和融合仍然具有挑战性。在这项研究中，我们在基于大脑区域的结构和功能图谱融合的基础上，在 NDs 患者和健康对照者之间的二分类任务中，开发了一种增强型多模态图卷积网络（MME-GCN）。首先，我们基于相同的大脑图谱，分别从 sMRI 和 fMRI 数据中构建结构和功能图谱。其次，我们使用机器学习从结构图谱网络中提取重要特征。第三，我们使用这些提取的特征来调整功能图谱网络中的相应边权重。最后，我们训练一个多层 GCN 并将其用于二分类任务。MME-GCN 在由神经精神疾病表型联盟提供的公开数据集上实现了 93.71%的分类准确率。此外，我们还分析了从结构图谱中选择的重要特征，并在功能图谱中对其进行了验证。使用 MME-GCN，我们发现了一些对 NDs 很重要的特定脑连接。
