Guangdong Key Lab of Communication and Computer Network, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.
Zhongshan Institute of Modern Industrial Technology, South China University of Technology, Zhongshan, China.
BMC Bioinformatics. 2021 Jul 22;22(1):379. doi: 10.1186/s12859-021-04295-1.
Autism spectrum disorders (ASD) imply a spectrum of symptoms rather than a single phenotype. ASD could affect brain connectivity at different degree based on the severity of the symptom. Given their excellent learning capability, graph neural networks (GNN) methods have recently been used to uncover functional connectivity patterns and biological mechanisms in neuropsychiatric disorders, such as ASD. However, there remain challenges to develop an accurate GNN learning model and understand how specific decisions of these graph models are made in brain network analysis.
In this paper, we propose a graph attention network based learning and interpreting method, namely GAT-LI, which learns to classify functional brain networks of ASD individuals versus healthy controls (HC), and interprets the learned graph model with feature importance. Specifically, GAT-LI includes a graph learning stage and an interpreting stage. First, in the graph learning stage, a new graph attention network model, namely GAT2, uses graph attention layers to learn the node representation, and a novel attention pooling layer to obtain the graph representation for functional brain network classification. We experimentally compared GAT2 model's performance on the ABIDE I database from 1035 subjects against the classification performances of other well-known models, and the results showed that the GAT2 model achieved the best classification performance. We experimentally compared the influence of different construction methods of brain networks in GAT2 model. We also used a larger synthetic graph dataset with 4000 samples to validate the utility and power of GAT2 model. Second, in the interpreting stage, we used GNNExplainer to interpret learned GAT2 model with feature importance. We experimentally compared GNNExplainer with two well-known interpretation methods including Saliency Map and DeepLIFT to interpret the learned model, and the results showed GNNExplainer achieved the best interpretation performance. We further used the interpretation method to identify the features that contributed most in classifying ASD versus HC.
We propose a two-stage learning and interpreting method GAT-LI to classify functional brain networks and interpret the feature importance in the graph model. The method should also be useful in the classification and interpretation tasks for graph data from other biomedical scenarios.
自闭症谱系障碍（ASD）意味着一系列症状，而不是单一的表型。ASD 可能会根据症状的严重程度对大脑连接产生不同程度的影响。鉴于它们出色的学习能力，图神经网络（GNN）方法最近已被用于揭示神经精神疾病（如 ASD）中的功能连接模式和生物学机制。然而，在开发准确的 GNN 学习模型以及理解这些图模型在脑网络分析中的特定决策方面仍存在挑战。
在本文中，我们提出了一种基于图注意网络的学习和解释方法，即 GAT-LI，它可以学习对 ASD 个体与健康对照组（HC）的功能脑网络进行分类，并通过特征重要性来解释学习到的图模型。具体来说，GAT-LI 包括图学习阶段和解释阶段。首先，在图学习阶段，一个新的图注意网络模型，即 GAT2，使用图注意层来学习节点表示，并使用新的注意池化层来获得功能脑网络分类的图表示。我们在来自 1035 个样本的 ABIDE I 数据库上实验比较了 GAT2 模型与其他著名模型的分类性能，结果表明 GAT2 模型的分类性能最佳。我们实验比较了 GAT2 模型中脑网络不同构建方法的影响。我们还使用了一个具有 4000 个样本的更大的合成图数据集来验证 GAT2 模型的实用性和功效。其次，在解释阶段，我们使用 GNNExplainer 通过特征重要性来解释学习到的 GAT2 模型。我们在实验中比较了 GNNExplainer 与两种著名的解释方法（包括 Saliency Map 和 DeepLIFT）来解释学习到的模型，结果表明 GNNExplainer 的解释性能最佳。我们进一步使用解释方法来识别对 ASD 与 HC 分类贡献最大的特征。
我们提出了一种两阶段的学习和解释方法 GAT-LI，用于对功能脑网络进行分类，并解释图模型中的特征重要性。该方法也应该有助于其他生物医学场景中的图数据的分类和解释任务。
