干旱区生态环境知识资源中心(Arid): Signed graph representation learning for functional-to-structural brain network mapping

Arid

DOI	10.1016/j.media.2022.102674
	Signed graph representation learning for functional-to-structural brain network mapping
	Tang, Haoteng; Guo, Lei; Fu, Xiyao; Wang, Yalin; Mackin, Scott; Ajilore, Olusola; Leow, Alex D.; Thompson, Paul M.; Huang, Heng; Zhan, Liang
通讯作者	Tang, HT ; Zhan, L
来源期刊	MEDICAL IMAGE ANALYSIS
ISSN	1361-8415
EISSN	1361-8423
出版年	2023
卷号	83
英文摘要	MRI-derived brain networks have been widely used to understand functional and structural interactions among brain regions, and factors that affect them, such as brain development and diseases. Graph mining on brain networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Since brain functional and structural networks describe the brain topology from different perspec-tives, exploring a representation that combines these cross-modality brain networks has significant clinical implications. Most current studies aim to extract a fused representation by projecting the structural network to the functional counterpart. Since the functional network is dynamic and the structural network is static, mapping a static object to a dynamic object may not be optimal. However, mapping in the opposite direction (i.e., from functional to structural networks) are suffered from the challenges introduced by negative links within signed graphs. Here, we propose a novel graph learning framework, named as Deep Signed Brain Graph Mining or DSBGM, with a signed graph encoder that, from an opposite perspective, learns the cross-modality representations by projecting the functional network to the structural counterpart. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets (HCP and OASIS). Our experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods.
英文关键词	Signed graph learning Multimodal Structural network Functional network Reconstruction Prediction
类型	Article
语种	英语
开放获取类型	Bronze, Green Accepted
收录类别	SCI-E
WOS记录号	WOS:000893237300007
WOS关键词	CONNECTIVITY ; PARCELLATION ; BALANCE
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS研究方向	Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397820
推荐引用方式 GB/T 7714	Tang, Haoteng,Guo, Lei,Fu, Xiyao,et al. Signed graph representation learning for functional-to-structural brain network mapping[J],2023,83.
APA	Tang, Haoteng.,Guo, Lei.,Fu, Xiyao.,Wang, Yalin.,Mackin, Scott.,...&Zhan, Liang.(2023).Signed graph representation learning for functional-to-structural brain network mapping.MEDICAL IMAGE ANALYSIS,83.
MLA	Tang, Haoteng,et al."Signed graph representation learning for functional-to-structural brain network mapping".MEDICAL IMAGE ANALYSIS 83(2023).