首页|Multimodal Fusion of Brain Imaging Data:Methods and Applications

Multimodal Fusion of Brain Imaging Data:Methods and Applications

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Neuroimaging data typically include multiple modalities,such as structural or functional magnetic resonance imaging,dif-fusion tensor imaging,and positron emission tomography,which provide multiple views for observing and analyzing the brain.To lever-age 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 ex-plore 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 under-stand 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 chal-lenges imposed by multi-scale and big data,which arises an urgent demand on developing new models and platforms.

Multimodal fusionsupervised learningunsupervised learningbrain atlascognitionbrain disorders

Na Luo、Weiyang Shi、Zhengyi Yang、Ming Song、Tianzi Jiang

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Brainnetome Center and National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China

School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China

Center for Excellence in Brain Science and Intelligence Technology,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China

Research Center for Augmented Intelligence,Zhejiang Laboratory,Hangzhou 311100,China

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National Natural Science Foundation of ChinaNational Natural Science Foundation of Chinascience and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence Project of ChinaNational Key Research and Development Program of ChinaChina Postdoctoral Science FoundationChinese Academy of Sciences,Science and Technology Service Network InitiativeStrategic Priority Research Program of the Chinese Academy of Sciences,ChinaScientific Project of Zhejiang Laboratory,China

82001450821513072021 ZD02002012022YFC3601200BX20200364KFJ-STS-ZDTP-078XDB320302002022ND0AN01

2024

10.1007/s11633-023-1442-8

机器智能研究(英文)
中国科学院自动化所

机器智能研究(英文)

CSTPCDEI
影响因子：0.49
ISSN：2731-538X
年,卷(期)：2024.21(1)
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