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Constructing brain functional network by Adversarial Temporal-Spatial Aligned Transformer for early AD analysis

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成果类型:
期刊论文
作者:
Zuo, Qiankun;Lu, Libin;Wang, Lin;Zuo, Jiahui;Ouyang, Tao
通讯作者:
Wang, L.
作者机构:
[Zuo, Qiankun] Hubei Univ Econ, Sch Informat Engn, Wuhan, Peoples R China.
[Wang, Lin; Zuo, Qiankun] Chinese Acad Sci, Shenzhen Inst Adv Technol, CAS Key Lab Human Machine Intelligence Synergy Sys, Shenzhen, Peoples R China.
[Wang, Lin; Zuo, Qiankun] Robot Soc, SIAT Branch, Shenzhen Inst Artificial Intelligence, Shenzhen, Peoples R China.
[Lu, Libin] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Peoples R China.
[Wang, Lin] Guangdong Hong Kong Macau Joint Lab Human Machine, Shenzhen, Peoples R China.
通讯机构:
[Wang, L.] C
CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, China
语种:
英文
关键词:
functional brain connectivity;temporal-spatial transformer alignment;Generative adversarial learning;Graph convolutional network;Early Alzheimer's disease
期刊:
Frontiers in Neuroscience
ISSN:
1662-4548
年:
2022
卷:
16
页码:
1087176
基金类别:
This work was supported in part by National Key R&D Program of China under grant 2020YFC2005803, International Partnership Program of Chinese Academy of Sciences under grant GJHZ2021132, and NSFC 62003331.
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:
Introduction: The brain functional network can describe the spontaneous activity of nerve cells and reveal the subtle abnormal changes associated with brain disease. It has been widely used for analyzing early Alzheimer's disease (AD) and exploring pathological mechanisms. However, the current methods of constructing functional connectivity networks from functional magnetic resonance imaging (fMRI) heavily depend on the software toolboxes, which may lead to errors in connection strength estimation and bad performance in disease analysis because...

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