A NOVEL MULTI-MODALITY FRAMEWORK FOR EXPLORING BRAIN CONNECTIVITY HUBS VIA REINFORCEMENT LEARNING APPROACH

被引：0

作者：

Zhang, Shu ^{[1
]}

Zhang, Haiyang ^{[1
]}

Wang, Ruoyang ^{[1
]}

Kang, Yanqing ^{[1
]}

Yu, Sigang ^{[1
]}

Wu, Jinru ^{[1
]}

机构：

[1] Northwestern Polytech Univ, Sch Comp Sci, Xian, Peoples R China

来源：

2023 IEEE 20TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, ISBI | 2023年

基金：

中国国家自然科学基金;

关键词：

Reinforcement learning; multi-modality analysis; brain structure; brain function; brain connectivity hubs;

D O I：

10.1109/ISBI53787.2023.10230789

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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.

引用

页数：5