Outcome prediction of unconscious patients based on weighted sparse brain network construction
2023
发表期刊IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (IF:6.7[JCR-2023],7.1[5-Year])
ISSN2168-2194
EISSN2168-2208
卷号27期号:1页码:1-11
发表状态已发表
DOI10.1109/JBHI.2022.3218652
摘要

It is quite challenging to establish a prompt and reliable prognosis assessment for acquired brain injury (ABI) patients with persistent severe disorders of consciousness (DOC) like unconscious comatose and unresponsive wakefulness syndrome (a.k.a., vegetative state). Recent advances in brain functional imaging and functional net-work analysis have demonstrated its potential in determining the consciousness level and prognostic outcome for ABI patients with DOC. However, the diagnostic and prognostic usefulness of the whole-brain functional connectome based on advanced machine learning techniques has not been fully evaluated. The first aim of this study is to predict the outcome of individual unconscious ABI patients during a three-month follow-up. The second aim is to conduct precise individualized differentiation among different consciousness levels for exploring the neurobiological mechanisms underlying DOC. Based on resting-state fMRI, we construct large-scale functional networks by using a weighted sparse model, which ensures sparsity and interpretability by preserving strong functional connections. The functional connection strengths are exploited as features for outcome prediction and consciousness level differentiation. We achieve significantly improved consciousness level classification (accuracy: 84.78) and recovery outcome prediction (accuracy: 89.74) compared to other network construction methods. More importantly, we reveal the contributive connections across the entire brain in both tasks. These connections could serve as the potential biomarkers for better understanding of consciousness and further provide new insight into the development of diagnostic, prognostic, and effective therapeutic guidelines for ABI patients with DOC. IEEE

关键词Computer system recovery Diagnosis Forecasting Learning systems Patient rehabilitation Acquired brain injuries Brain injury Brain modeling Consciousness level differentiation Correlation Disorder of consciousness Functional magnetic resonance imaging Outcome prediction Recovery outcome prediction Resting state Resting-state functional MRI
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收录类别SCI ; EI ; SCOPUS
语种英语
资助项目National Natural Science Foundation of China[
WOS研究方向Computer Science ; Mathematical & Computational Biology ; Medical Informatics
WOS类目Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics
WOS记录号WOS:000927904300047
出版者Institute of Electrical and Electronics Engineers Inc.
EI入藏号20224613111175
EI主题词Magnetic resonance imaging
EI分类号461.5 Rehabilitation Engineering and Assistive Technology ; 461.6 Medicine and Pharmacology ; 701.2 Magnetism: Basic Concepts and Phenomena ; 746 Imaging Techniques
原始文献类型Article in Press
来源库IEEE
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文献类型期刊论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/248914
专题生物医学工程学院_PI研究组_张寒组
生物医学工程学院_PI研究组_沈定刚组
生物医学工程学院_公共科研平台_智能医学科研平台
生物医学工程学院_PI研究组_马智炜组
通讯作者Zhang, Han; Mao, Ying; Shen, Dinggang
作者单位
1.Zhengzhou Univ, Sch Elect & Informat Engn, Zhengzhou 450001, Peoples R China
2.ShanghaiTech Univ, Sch Biomed Engn, Shanghai 201210, Peoples R China
3.Fudan Univ, Huashan Hosp, Shanghai Med Coll, Dept Neurosurg, Shanghai 200010, Peoples R China
4.Natl Ctr Neurol Disorders, Shanghai 200010, Peoples R China
5.Shanghai Key Lab Brain Funct & Restorat & Neural, Shanghai 200010, Peoples R China
6.Henan Univ Technol, Sch Artificial Intelligence & Big Data, Zhengzhou 450001, Peoples R China
7.Fudan Univ, Huashan Hosp, Dept Radiol, Shanghai 200040, Peoples R China
8.Shanghai United Imaging Intelligence Co Ltd, Shanghai 200230, Peoples R China
9.Shanghai Clin Res & Trial Ctr, Shanghai 201210, Peoples R China
通讯作者单位生物医学工程学院
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GB/T 7714
Yu, Renping,Zhang, Han,Wu, Xuehai,et al. Outcome prediction of unconscious patients based on weighted sparse brain network construction[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2023,27(1):1-11.
APA Yu, Renping.,Zhang, Han.,Wu, Xuehai.,Fei, Xuan.,Yang, Qing.,...&Shen, Dinggang.(2023).Outcome prediction of unconscious patients based on weighted sparse brain network construction.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,27(1),1-11.
MLA Yu, Renping,et al."Outcome prediction of unconscious patients based on weighted sparse brain network construction".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 27.1(2023):1-11.
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