Title: Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification Show Chinese title

Title: 图自动编码器在脑网络嵌入学习和重度抑郁症识别中的应用

Abstract: Brain functional connectivity (FC) reveals biomarkers for identification of various neuropsychiatric disorders. Recent application of deep neural networks (DNNs) to connectome-based classification mostly relies on traditional convolutional neural networks using input connectivity matrices on a regular Euclidean grid. We propose a graph deep learning framework to incorporate the non-Euclidean information about graph structure for classifying functional magnetic resonance imaging (fMRI)-derived brain networks in major depressive disorder (MDD). We design a novel graph autoencoder (GAE) architecture based on the graph convolutional networks (GCNs) to embed the topological structure and node content of large-sized fMRI networks into low-dimensional latent representations. In network construction, we employ the Ledoit-Wolf (LDW) shrinkage method to estimate the high-dimensional FC metrics efficiently from fMRI data. We consider both supervised and unsupervised approaches for the graph embedding learning. The learned embeddings are then used as feature inputs for a deep fully-connected neural network (FCNN) to discriminate MDD from healthy controls. Evaluated on two resting-state fMRI (rs-fMRI) MDD datasets, results show that the proposed GAE-FCNN model significantly outperforms several state-of-the-art methods for brain connectome classification, achieving the best accuracy using the LDW-FC edges as node features. The graph embeddings of fMRI FC networks learned by the GAE also reveal apparent group differences between MDD and HC. Our new framework demonstrates feasibility of learning graph embeddings on brain networks to provide discriminative information for diagnosis of brain disorders. Show Chinese abstract

Abstract: 脑功能连接（FC）揭示了识别各种神经精神障碍的生物标志物。 深度神经网络（DNNs）在基于连接组的分类中的最近应用主要依赖于传统的卷积神经网络，使用在规则欧几里得网格上的输入连接矩阵。 我们提出了一种图深度学习框架，以整合关于图结构的非欧几里得信息，用于分类重度抑郁症（MDD）的功能磁共振成像（fMRI）衍生脑网络。 我们设计了一种基于图卷积网络（GCNs）的新图自编码器（GAE）架构，以将大规模fMRI网络的拓扑结构和节点内容嵌入到低维潜在表示中。 在网络构建中，我们采用Ledoit-Wolf（LDW）收缩方法，从fMRI数据中高效地估计高维FC指标。 我们考虑了图嵌入学习的监督和无监督方法。 然后，所学的嵌入作为特征输入，用于深度全连接神经网络（FCNN），以区分MDD和健康对照组。 在两个静息态fMRI（rs-fMRI）MDD数据集上评估，结果表明所提出的GAE-FCNN模型显著优于几种最先进的脑连接组分类方法，使用LDW-FC边作为节点特征达到最佳准确率。 由GAE学习的fMRI FC网络的图嵌入也揭示了MDD和HC之间的明显群体差异。 我们的新框架展示了在脑网络上学习图嵌入的可行性，以提供诊断脑部疾病的关键信息。