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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]) |
ISSN | 2168-2194 |
EISSN | 2168-2208 |
卷号 | 27期号:1页码:1-11 |
发表状态 | 已发表 |
DOI | 10.1109/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 |
URL | 查看原文 |
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 |
引用统计 | 正在获取...
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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 |
推荐引用方式 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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