干旱区生态环境知识资源中心(Arid): Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model

Arid

DOI	10.1109/TNNLS.2022.3220220
	Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model
	Tang, Haoteng; Ma, Guixiang; Guo, Lei; Fu, Xiyao; Huang, Heng; Zhang, Liang
通讯作者	Zhang, L
来源期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
EISSN	2162-2388
出版年	2024
卷号	35 期号:6 页码:7363-7375
英文摘要	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.
英文关键词	Brain functional networks contrastive learning data augmentation hierarchical graph pooling (HGP) interpretability signed graph learning
类型	Article
语种	英语
开放获取类型	Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000886828100001
WOS关键词	MENTAL-STATE-EXAMINATION ; PREDICTION ; ARCHITECTURE ; HUBS
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS研究方向	Computer Science ; Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404153
推荐引用方式 GB/T 7714	Tang, Haoteng,Ma, Guixiang,Guo, Lei,et al. Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model[J],2024,35(6):7363-7375.
APA	Tang, Haoteng,Ma, Guixiang,Guo, Lei,Fu, Xiyao,Huang, Heng,&Zhang, Liang.(2024).Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,35(6),7363-7375.
MLA	Tang, Haoteng,et al."Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 35.6(2024):7363-7375.