CASIA OpenIR  > 学术期刊  > Machine Intelligence Research
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

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
Sub direction classification其他
planning direction of the national heavy laboratory其他
Paper associated data
Chinese guide
Citation statistics
Document Type期刊论文
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.
Files in This Item:
File Name/Size DocType Version Access License
MIR-2022-12-391.R1.p(1726KB)期刊论文出版稿开放获取CC BY-NC-SAView
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Na Luo]'s Articles
[Weiyang Shi]'s Articles
[Zhengyi Yang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Na Luo]'s Articles
[Weiyang Shi]'s Articles
[Zhengyi Yang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Na Luo]'s Articles
[Weiyang Shi]'s Articles
[Zhengyi Yang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: MIR-2022-12-391.R1.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.