首页> Health information science and systems> SPSGL: uncovering psychiatric network mechanisms via structural-prior guided synaptic graph learning.

SPSGL: uncovering psychiatric network mechanisms via structural-prior guided synaptic graph learning.

导出 LinkOut

翻译打印收藏纠错

第一作者： Wei,Wu

第一单位： School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051 China.

作者： Wei,Wu ^[1] ; Jiawei,Guan ^[2] ; Qi,Zhao ^[1] ; Jianwei,Shuai ^[3]

作者单位： School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051 China. ^[1] School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi'an Jiaotong-Liverpool University, Suzhou, 215123 China. ^[2] Joint Medical Engineering Interdisciplinary Research Center, Wenzhou Institute UCAS, and the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000 Zhejiang China. ^[3]

关键词 Dynamic connectivity Frequency-domain representation Functional brain graph Multi-head attention Structural priors

DOI 10.1007/s13755-026-00467-6

PMID 42339397

发布时间 2026-06-24

翻译满意度评价：

准确一般字义偏差语意偏离译文作用小提交

浏览0

Health information science and systems

2026年14卷1期

69页

相似文献

中文期刊
外文期刊
学位论文
会议论文

浏览0

Health information science and systems

2026年14卷1期

69页

检索历史清除

SPSGL: uncovering psychiatric network mechanisms via structural-prior guided synaptic graph learning.

Health information science and systems

相似文献

同项目论文

Health information science and systems

相关期刊

参考文献：
指定格式：	NoteExpress EndNote RefWorks NoteFirst

检索历史 清除

SPSGL: uncovering psychiatric network mechanisms via structural-prior guided synaptic graph learning.

Health information science and systems

相似文献

同项目论文

Health information science and systems

相关期刊

检索历史清除