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Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics
Song Ming1,2; Yi Yang3; Jianghong He3; Zhengyi Yang1,2; Shan Yu1,2; Qiuyou Xie4; Xiaoyu Xia3; Yuanyuan Dang3; Qiang Zhang3; Xinhuai Wu5; Yue Cui1,2; Bing Hou1,2; Ronghao Yu4; Ruxiang Xu3; Tianzi Jiang1,2,6,7,8
Source PublicationeLife
2018-08-14
Volume2018Issue:7Pages:e36173
AbstractDisorders of consciousness are a heterogeneous mixture of different diseases or injuries. Although some indicators and models have been proposed for prognostication, any single method when used alone carries a high risk of false prediction. This study aimed to develop a multidomain prognostic model that combines resting state functional MRI with three clinical characteristics to predict one year outcomes at the single-subject level. The model discriminated between patients who would later recover consciousness and those who would not with an accuracy of around 88% on three datasets from two medical centers. It was also able to identify the prognostic importance of different predictors, including brain functions and clinical characteristics. To our knowledge, this is the first reported implementation of a multidomain prognostic model based on resting state functional MRI and clinical characteristics in chronic disorders of consciousness, which we suggest is accurate, robust, and interpretable.
KeywordDisorders Of Consciousness Prognosis Resting State Fmri Functional Connectivity Brain Network
DOIhttps://doi.org/10.7554/eLife.36173
Indexed BySCI ; SSCi
WOS IDWOS:000444967900001
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22078
Collection脑网络组研究中心
Corresponding AuthorTianzi Jiang
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences
2.Brainnetome Center, Institute of Automation, The Chinese Academy of Sciences,
3.Department of Neurosurgery, PLA Army General Hospital
4.Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command
5.Department of Radiology, PLA Army General Hospital
6.CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences
7.Key Laboratory for Neuroinformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China
8.The Queensland Brain Institute, University of Queensland
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Song Ming,Yi Yang,Jianghong He,et al. Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics[J]. eLife,2018,2018(7):e36173.
APA Song Ming.,Yi Yang.,Jianghong He.,Zhengyi Yang.,Shan Yu.,...&Tianzi Jiang.(2018).Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics.eLife,2018(7),e36173.
MLA Song Ming,et al."Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics".eLife 2018.7(2018):e36173.
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