[论文解读] Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model

Haoteng Tang, Guixiang Ma+4 more|arXiv (Cornell University)|Jul 14, 2022

Functional Brain Connectivity Studies被引用 4

一句话总结

本文提出了一种新颖的分层有符号图池化模型，用于脑功能网络表征学习，通过对比学习与数据增强相结合，提升了临床预测任务的性能。该方法在HCP和OASIS数据集上的分类与回归任务中均取得了最先进结果，同时通过图显著性图实现了可解释的生物标志物发现。

ABSTRACT

Recently brain networks have been widely adopted to study brain dynamics, brain development and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. However, current graph learning techniques have several issues on brain network mining. Firstly, most current graph learning models are designed for unsigned graph, which hinders the analysis of many signed network data (e.g., brain functional networks). Meanwhile, the insufficiency of brain network data limits the model performance on clinical phenotypes predictions. Moreover, few of current graph learning model is interpretable, which may not be capable to provide biological insights for model outcomes. Here, we propose an interpretable hierarchical signed graph representation learning model to extract graph-level representations from brain functional networks, which can be used for different prediction tasks. In order to further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning. We evaluate this framework on different classification and regression tasks using the data from HCP and OASIS. Our results from extensive experiments demonstrate the superiority of the proposed model compared to several state-of-the-art techniques. Additionally, we use graph saliency maps, derived from these prediction tasks, to demonstrate detection and interpretation of phenotypic biomarkers.

研究动机与目标

• 解决现有图神经网络在处理fMRI数据中常见的有符号脑功能网络时的局限性。
• 通过提出一种新颖的数据增强策略，缓解脑网络数据稀缺的问题，以支持对比学习。
• 增强模型可解释性，通过显著性映射揭示预测表型相关的生物学洞察。
• 开发一种分层图池化机制，以捕捉多尺度脑网络结构，从而提升表征学习效果。

提出的方法

• 提出一种分层有符号图池化框架，通过在图层次的多个层级聚合节点特征，学习脑功能网络的低维表征。
• 基于平衡理论引入有符号图卷积网络，以建模脑区间正负功能连接关系。
• 设计一种对比学习目标，利用增强的脑网络样本提升泛化能力与表征质量。
• 通过边扰动与节点特征掩码实现数据增强，生成对比预训练的正样本对。
• 利用注意力机制生成的图显著性图，突出与临床表型相关的关键脑区。
• 将全局读出与分层池化相结合，生成适用于下游分类与回归任务的图级别表征。

Figure 1: Diagram of the proposed contrastive graph learning framework (in the bottom black box) with hierarchical signed graph representation learning model (in the top black box) for functional brain network embedding and downstream tasks (i.e., phenotype classification or regression).

实验结果

研究问题

• RQ1 分层有符号图池化模型是否能在脑网络表征学习中超越现有无符号图学习方法，在临床预测任务中表现更优？
• RQ2 在脑网络数据有限的条件下，所提出的对比学习策略结合数据增强，在提升模型性能方面有多高效？
• RQ3 模型的预测结果在多大程度上可通过显著性图实现可解释性，以识别与特定表型相关的生物相关脑区？
• RQ4 在有符号图学习中引入平衡理论，是否能增强对功能脑连接模式的建模能力？

主要发现

• 所提模型在HCP和OASIS数据集的分类与回归任务中均达到最先进性能，优于现有SOTA方法。
• 结合数据增强的对比学习显著提升了模型泛化能力，尤其在神经影像中常见的低数据场景下表现突出。
• 图显著性图成功识别出与临床表型（如MMSE、Flanker任务表现及攻击性行为）相关的生物上合理的脑区。
• 模型检测到已知的神经解剖区域（如楔前叶、岛叶和扣带皮层）为认知与行为特质的关键贡献区域。
• 与平面GNN相比，分层池化能更有效地捕捉多尺度脑网络组织结构，从而提升表征质量。
• 基于平衡理论的有符号图学习使功能网络中的节点关系更具可解释性与生物学一致性。

Figure 2: Visualization of the averaged adjacency matrices for original and contrastive samples. The averaged contrastive sample pair is generated by using a window size $d=10$ .