Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model
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DOI：
10.1109/tnnls.2022.3220220
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发表时间：
2022-07
影响因子：
10.4
通讯作者：
Haoteng Tang;Guixiang Ma;Lei Guo;Xiyao Fu;Heng Huang;L. Zhang
中科院分区：
文献类型：
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作者：
Haoteng Tang;Guixiang Ma;Lei Guo;Xiyao Fu;Heng Huang;L. Zhang
近年来，脑网络已被广泛用于研究脑动力学、脑发育和脑疾病。脑功能网络的图形表示学习技术可以促进临床表型和神经退行性疾病的新生物标志物的发现。然而，目前的图学习技术在大脑网络挖掘方面存在一些问题。首先，大多数当前的图学习模型都是针对无符号图设计的，这阻碍了对许多有符号网络数据的分析（例如，脑功能网络）。同时，脑网络数据的不足限制了模型对临床表型预测的性能。此外，目前的图学习模型很少是可解释的，这可能无法为模型结果提供生物学见解。在这里，我们提出了一个可解释的分层符号图表示学习（HSGPL）模型，从大脑功能网络中提取图级表示，可用于不同的预测任务。为了进一步提高模型的性能，我们还提出了一种新的策略来增强功能性大脑网络数据，以进行对比学习。我们使用来自人类连接体项目（HCP）和开放获取系列成像研究（OASIS）的数据，在不同的分类和回归任务上评估该框架。我们从大量的实验结果表明，与几个国家的最先进的技术相比，该模型的优越性。此外，我们使用来自这些预测任务的图形显着性图来展示表型生物标志物的检测和解释。
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. First, 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 the current graph learning models are interpretable, which may not be capable of providing biological insights for model outcomes. Here, we propose an interpretable hierarchical signed graph representation learning (HSGPL) model to extract graph-level representations from brain functional networks, which can be used for different prediction tasks. 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 data from human connectome project (HCP) and open access series of imaging studies (OASIS). Our results from extensive experiments demonstrate the superiority of the proposed model compared with several state-of-the-art techniques. In addition, we use graph saliency maps, derived from these prediction tasks, to demonstrate detection and interpretation of phenotypic biomarkers.