Multimodal Fusion of Brain Imaging Data: Methods and Applications
作者机构：Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing 100049China Center for Excellence in Brain Science and Intelligence TechnologyInstitute of AutomationChinese Academy of SciencesBeijing 100190China Research Center for Augmented IntelligenceZhejiang LaboratoryHangzhou 311100China
出 版 物：《Machine Intelligence Research》 (机器智能研究（英文版）)
年 卷 期：2024年第21卷第1期
页 面：136-152页
核心收录：
学科分类：1001[医学-基础医学(可授医学、理学学位)] 10[医学]
基 金：国家自然科学基金 Science and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence Project of China 国家科技攻关计划项目 中国博士后科学基金 the Chinese Academy of Sciences,Science and Technology Service Network Initiative the Strategic Priority Research Program of the Chinese Academy of Sciences,China the Scientific Project of Zhejiang Laboratory,China
主 题：Multimodal fusion supervised learning unsupervised learning brain atlas cognition brain disorders
摘 要：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 *** lever-age the complementary representations of different modalities,multimodal fusion is consequently needed to dig out both inter-modality and intra-modality *** 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 *** 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 *** 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 ***,we discuss some exciting emerging trends and important future ***,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.
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