Detecting Residual Awareness in Patients With Prolonged Disorders of Consciousness: An fNIRS Study
文献类型：期刊论文
| 作者 | Li, Meng1 ; Yang, Yi2; Zhang, Yujin3,4; Gao, Yuhang1; Jing, Rixing1; Dang, Yuanyuan2; Chen, Xueling2; He, Jianghong2; Si, Juanning1
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| 刊名 | FRONTIERS IN NEUROLOGY
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| 出版日期 | 2021-07-28 |
| 卷号 | 12页码:10 |
| 关键词 | disorders of consciousness functional near-infrared spectroscopy motor imagery support vector machine minimally consciousness state |
| ISSN号 | 1664-2295 |
| DOI | 10.3389/fneur.2021.618055 |
| 通讯作者 | He, Jianghong(he_jianghong@sina.cn) ; Si, Juanning(sijuanning@bistu.edu.cn) |
| 英文摘要 | Recent advances in neuroimaging technologies have provided insights into detecting residual consciousness and assessing cognitive abilities in patients with disorders of consciousness (DOC). Functional near-infrared spectroscopy (fNIRS) is non-invasive and portable and can be used for longitudinal bedside monitoring, making it uniquely suited for evaluating brain function in patients with DOC at appropriate spatiotemporal resolutions. In this pilot study, an active command-driven motor imagery (MI) paradigm based on fNIRS was used to detect residual consciousness in patients with prolonged DOC. A support vector machine (SVM) classifier was used to classify yes-or-no responses. The results showed that relatively reliable responses were detected from three out of five patients in a minimally consciousness state (MCS). One of the patients answered all the questions accurately when assessed according to this method. This study confirmed the feasibility of using portable fNIRS technology to detect residual cognitive ability in patients with prolonged DOC by active command-driven motor imagery. We hope to detect the exact level of consciousness in DOC patients who may have a higher level of consciousness. |
| WOS关键词 | NEAR-INFRARED SPECTROSCOPY ; BRAIN ACTIVITY |
| 资助项目 | Natural Science Foundation of Beijing, China[4214080] ; Beijing Science and Technology Nova[Z181100006218050] ; National Key Research and Development Program of China[2017YFB1002502] ; National Natural Science Foundation of China[81871398] ; National Natural Science Foundation of China[81600919] ; Key R&D Program of Guangdong Province, China[2018B030339001] ; Beijing Municipal Education Commission Science and Technology Program[KM202011232008] ; Supplementary and Supportive Project for Teachers at Beijing Information Science and Technology University (2018-2020)[5029011103] |
| WOS研究方向 | Neurosciences & Neurology |
| 语种 | 英语 |
| WOS记录号 | WOS:000683566800001 |
| 出版者 | FRONTIERS MEDIA SA |
| 资助机构 | Natural Science Foundation of Beijing, China ; Beijing Science and Technology Nova ; National Key Research and Development Program of China ; National Natural Science Foundation of China ; Key R&D Program of Guangdong Province, China ; Beijing Municipal Education Commission Science and Technology Program ; Supplementary and Supportive Project for Teachers at Beijing Information Science and Technology University (2018-2020) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/45685]  |
| 专题 | 自动化研究所_类脑智能研究中心 |
| 通讯作者 | He, Jianghong; Si, Juanning |
| 作者单位 | 1.Beijing Informat Sci & Technol Univ, Sch Instrumentat Sci & Optoelect Engn, Beijing, Peoples R China 2.Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Meng,Yang, Yi,Zhang, Yujin,et al. Detecting Residual Awareness in Patients With Prolonged Disorders of Consciousness: An fNIRS Study[J]. FRONTIERS IN NEUROLOGY,2021,12:10. |
| APA | Li, Meng.,Yang, Yi.,Zhang, Yujin.,Gao, Yuhang.,Jing, Rixing.,...&Si, Juanning.(2021).Detecting Residual Awareness in Patients With Prolonged Disorders of Consciousness: An fNIRS Study.FRONTIERS IN NEUROLOGY,12,10. |
| MLA | Li, Meng,et al."Detecting Residual Awareness in Patients With Prolonged Disorders of Consciousness: An fNIRS Study".FRONTIERS IN NEUROLOGY 12(2021):10. |
入库方式： OAI收割
来源：自动化研究所
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