A Novel Multi-Modality Framework for Exploring Brain Connectivity Hubs Via Reinforcement Learning Approach

Shu Zhang; Haiyang Zhang; Ruoyang Wang; Yanqing Kang; Sigang Yu; Jinru Wu

doi:10.1109/ISBI53787.2023.10230789

A Novel Multi-Modality Framework for Exploring Brain Connectivity Hubs Via Reinforcement Learning Approach

Shu Zhang, Haiyang Zhang, Ruoyang Wang, Yanqing Kang, Sigang Yu, Jinru Wu

计算机学院

Northwestern Polytechnical University Xian

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

Exploring the brain connectivity and identifying the connectivity hubs is an important issue for better understanding the working mechanism of the brain as well as assisting to investigate the brain of disease and disorders. In recent years, on one hand, plenty research have been proposed to study brain connectivity hubs either on functional or structural perspective, but very few studies are focusing on integration them together; on the other hand, efficient learning approach to deal with the complex brain network is urgently needed. To address above mentioned issues, in this paper, we propose a novel Multi-Modality Reinforcement Learning (MM-RL) approach, 50 brain connectivity hubs are identified and discussed. This work sheds the new insights that reinforcement learning approach can be adopted to study the brain connectivity, identify the potential hubs and interpret the relationship between function and structure.

源语言	英语
主期刊名	2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
出版商	IEEE Computer Society
ISBN（电子版）	9781665473583
DOI	https://doi.org/10.1109/ISBI53787.2023.10230789
出版状态	已出版 - 2023
活动	20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, 哥伦比亚期限: 18 4月 2023 → 21 4月 2023

出版系列

姓名	Proceedings - International Symposium on Biomedical Imaging
卷	2023-April
ISSN（印刷版）	1945-7928
ISSN（电子版）	1945-8452

会议

会议	20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
国家/地区	哥伦比亚
市	Cartagena
时期	18/04/23 → 21/04/23

访问文件

10.1109/ISBI53787.2023.10230789

其它文件与链接

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引用此

Zhang, S., Zhang, H., Wang, R., Kang, Y., Yu, S., & Wu, J. (2023). A Novel Multi-Modality Framework for Exploring Brain Connectivity Hubs Via Reinforcement Learning Approach. 在 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023 (Proceedings - International Symposium on Biomedical Imaging; 卷 2023-April). IEEE Computer Society. https://doi.org/10.1109/ISBI53787.2023.10230789

@inproceedings{572958fc762049288f378f5f1abd4940,

title = "A Novel Multi-Modality Framework for Exploring Brain Connectivity Hubs Via Reinforcement Learning Approach",

abstract = "Exploring the brain connectivity and identifying the connectivity hubs is an important issue for better understanding the working mechanism of the brain as well as assisting to investigate the brain of disease and disorders. In recent years, on one hand, plenty research have been proposed to study brain connectivity hubs either on functional or structural perspective, but very few studies are focusing on integration them together; on the other hand, efficient learning approach to deal with the complex brain network is urgently needed. To address above mentioned issues, in this paper, we propose a novel Multi-Modality Reinforcement Learning (MM-RL) approach, 50 brain connectivity hubs are identified and discussed. This work sheds the new insights that reinforcement learning approach can be adopted to study the brain connectivity, identify the potential hubs and interpret the relationship between function and structure.",

keywords = "brain connectivity hubs, brain function, brain structure, multi-modality analysis, Reinforcement learning",

author = "Shu Zhang and Haiyang Zhang and Ruoyang Wang and Yanqing Kang and Sigang Yu and Jinru Wu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 ; Conference date: 18-04-2023 Through 21-04-2023",

year = "2023",

doi = "10.1109/ISBI53787.2023.10230789",

language = "英语",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

booktitle = "2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023",

}

Zhang, S, Zhang, H, Wang, R, Kang, Y, Yu, S & Wu, J 2023, A Novel Multi-Modality Framework for Exploring Brain Connectivity Hubs Via Reinforcement Learning Approach. 在 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023. Proceedings - International Symposium on Biomedical Imaging, 卷 2023-April, IEEE Computer Society, 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023, Cartagena, 哥伦比亚, 18/04/23. https://doi.org/10.1109/ISBI53787.2023.10230789

A Novel Multi-Modality Framework for Exploring Brain Connectivity Hubs Via Reinforcement Learning Approach. / Zhang, Shu; Zhang, Haiyang; Wang, Ruoyang 等.
2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023. IEEE Computer Society, 2023. (Proceedings - International Symposium on Biomedical Imaging; 卷 2023-April).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - A Novel Multi-Modality Framework for Exploring Brain Connectivity Hubs Via Reinforcement Learning Approach

AU - Zhang, Shu

AU - Zhang, Haiyang

AU - Wang, Ruoyang

AU - Kang, Yanqing

AU - Yu, Sigang

AU - Wu, Jinru

PY - 2023

Y1 - 2023

N2 - Exploring the brain connectivity and identifying the connectivity hubs is an important issue for better understanding the working mechanism of the brain as well as assisting to investigate the brain of disease and disorders. In recent years, on one hand, plenty research have been proposed to study brain connectivity hubs either on functional or structural perspective, but very few studies are focusing on integration them together; on the other hand, efficient learning approach to deal with the complex brain network is urgently needed. To address above mentioned issues, in this paper, we propose a novel Multi-Modality Reinforcement Learning (MM-RL) approach, 50 brain connectivity hubs are identified and discussed. This work sheds the new insights that reinforcement learning approach can be adopted to study the brain connectivity, identify the potential hubs and interpret the relationship between function and structure.

AB - Exploring the brain connectivity and identifying the connectivity hubs is an important issue for better understanding the working mechanism of the brain as well as assisting to investigate the brain of disease and disorders. In recent years, on one hand, plenty research have been proposed to study brain connectivity hubs either on functional or structural perspective, but very few studies are focusing on integration them together; on the other hand, efficient learning approach to deal with the complex brain network is urgently needed. To address above mentioned issues, in this paper, we propose a novel Multi-Modality Reinforcement Learning (MM-RL) approach, 50 brain connectivity hubs are identified and discussed. This work sheds the new insights that reinforcement learning approach can be adopted to study the brain connectivity, identify the potential hubs and interpret the relationship between function and structure.

KW - brain connectivity hubs

KW - brain function

KW - brain structure

KW - multi-modality analysis

KW - Reinforcement learning

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U2 - 10.1109/ISBI53787.2023.10230789

DO - 10.1109/ISBI53787.2023.10230789

M3 - 会议稿件

AN - SCOPUS:85172148282

T3 - Proceedings - International Symposium on Biomedical Imaging

BT - 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023

PB - IEEE Computer Society

T2 - 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023

Y2 - 18 April 2023 through 21 April 2023

ER -

A Novel Multi-Modality Framework for Exploring Brain Connectivity Hubs Via Reinforcement Learning Approach

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