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Multimodal Fusion of Brain Imaging Data: Methods and Applications
Na Luo1; Weiyang Shi1; Zhengyi Yang1; Ming Song1; Tianzi Jiang1,2,3,4
Source PublicationMachine Intelligence Research
ISSN2731-538X
2024
Volume21Issue:1Pages:136-152
Abstract

Neuroimaging data typically include multiple modalities, such as structural or functional magnetic resonance imaging, diffusion tensor imaging, and positron emission tomography, which provide multiple views for observing and analyzing the brain. To leverage the complementary representations of different modalities, multimodal fusion is consequently needed to dig out both inter-modality and intra-modality information. With the exploited rich information, it is becoming popular to combine multiple modality data to explore the structural and functional characteristics of the brain in both health and disease status. In this paper, we first review a wide spectrum of advanced machine learning methodologies for fusing multimodal brain imaging data, broadly categorized into unsupervised and supervised learning strategies. Followed by this, some representative applications are discussed, including how they help to understand the brain arealization, how they improve the prediction of behavioral phenotypes and brain aging, and how they accelerate the biomarker exploration of brain diseases. Finally, we discuss some exciting emerging trends and important future directions. Collectively, we intend to offer a comprehensive overview of brain imaging fusion methods and their successful applications, along with the challenges imposed by multi-scale and big data, which arises an urgent demand on developing new models and platforms.

KeywordMultimodal fusion, supervised learning, unsupervised learning, brain atlas, cognition, brain disorders
DOI10.1007/s11633-023-1442-8
Sub direction classification其他
planning direction of the national heavy laboratory其他
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Chinese guidehttps://mp.weixin.qq.com/s/NU4iGGKhDFQXU9oGip1LEw
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/56029
Collection学术期刊_Machine Intelligence Research
Affiliation1.Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
3.Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
4.Research Center for Augmented Intelligence, Zhejiang Laboratory, Hangzhou 311100, China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Na Luo,Weiyang Shi,Zhengyi Yang,et al. Multimodal Fusion of Brain Imaging Data: Methods and Applications[J]. Machine Intelligence Research,2024,21(1):136-152.
APA Na Luo,Weiyang Shi,Zhengyi Yang,Ming Song,&Tianzi Jiang.(2024).Multimodal Fusion of Brain Imaging Data: Methods and Applications.Machine Intelligence Research,21(1),136-152.
MLA Na Luo,et al."Multimodal Fusion of Brain Imaging Data: Methods and Applications".Machine Intelligence Research 21.1(2024):136-152.
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