Brain Causality Modeling Using Structure-Guided Spatiotemporal Diffusion Model for MCI Analysis

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成果类型：

会议论文

作者：

Yanfei Zhu;Jiangtao Wang;Xuan Cheng;Junyi Chen;Hui Wei;...

作者机构：

Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, 430205, Wuhan, Hubei, China

School of Information Engineering, Hubei University of Economics, 430205, Wuhan, Hubei, China

[Zhi Yang] College of Electronics and Information Engineering, Sichuan University, 610065, Chengdu, China

[Yanfei Zhu] School of Foreign Languages, Sun Yat-sen University, 510275, Guangzhou, China

[Libin Lu] School of Mathematics and Computer Science, Wuhan Polytechnic University, 430023, Wuhan, China

语种：

英文

年：

2025

页码：

3-11

会议名称：

Brain Informatics: 17th International Conference, BI 2024, Bangkok, Thailand, December 13–15, 2024, Proceedings, Part II

出版地：

Berlin, Heidelberg

出版者：

Springer-Verlag

ISBN：

978-981-96-3296-1

DOI：

10.1007/978-981-96-3297-8_1

机构署名：

本校为其他机构

院系归属：

数学与计算机学院

摘要：

Early diagnosis of mild cognitive impairment (MCI) is crucial for the effective treatment and intervention of neurodegenerative diseases. Effective connectivity is one kind of brain network, which is helpful for analyzing the pathogenic mechanism of MCI. It is challenging to model causal relationships between brain regions from multimodal imaging data. This study proposes a new method for brain network causality modeling based on the structure-guided spatiotemporal diffusion model (SSDM), aiming to improve the accuracy of MCI diagnosis. By utilizing the advanced diffusion models, we introduced...

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Brain Causality Modeling Using Structure-Guided Spatiotemporal Diffusion Model for MCI Analysis

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