{"title":"体感诱发电位如何改善意识障碍的诊断:回顾性分析。","authors":"Xinwei Wang, Hongliang Gao, Jiulong Song, Peng Jing, Chao Wang, Nuanxin Yu, Shanshan Wu, Jianxiong Zhu, Zhiqiang Gao","doi":"10.1080/0954898X.2023.2269263","DOIUrl":null,"url":null,"abstract":"<p><p>The interpeak latency is a crucial characteristic of upper limb somatosensory evoked potentials (USEPs). However, the existing research on the correlation between interpeak latency and consciousness disorders is currently limited. We aimed to investigate how USEPs can contribute to the diagnosis of consciousness disorders. A retrospective analysis was conducted on 10 patients who underwent repetitive transcranial magnetic stimulation (rTMS) for consciousness disorders. The interpeak latency N13-N20, Glasgow coma scale (GCS), and Chinese Nanjing persistent vegetative state scale (CNPVSS) were evaluated before and after rTMS treatment, and the linear correlation between N13-N20, GCS, and CNPVSS was analysed. The scores of CNPVSS and GCS significantly increased in the first, second, and third months after rTMS. The N13-N20 was shorter in the second and third months after rTMS compared to before treatment. rTMS was found to shorten the N13-N20 latency, and there was a negative correlation between N13-N20 and the score of consciousness disorders. N13-N20 can serve as an objective index for evaluating consciousness disorders. This research provides potential insights for doctors in diagnosing patients with consciousness disorders.</p>","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"How somatosensory evoked potentials improve the diagnosis of the disturbance of consciousness: A retrospective analysis.\",\"authors\":\"Xinwei Wang, Hongliang Gao, Jiulong Song, Peng Jing, Chao Wang, Nuanxin Yu, Shanshan Wu, Jianxiong Zhu, Zhiqiang Gao\",\"doi\":\"10.1080/0954898X.2023.2269263\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The interpeak latency is a crucial characteristic of upper limb somatosensory evoked potentials (USEPs). However, the existing research on the correlation between interpeak latency and consciousness disorders is currently limited. We aimed to investigate how USEPs can contribute to the diagnosis of consciousness disorders. A retrospective analysis was conducted on 10 patients who underwent repetitive transcranial magnetic stimulation (rTMS) for consciousness disorders. The interpeak latency N13-N20, Glasgow coma scale (GCS), and Chinese Nanjing persistent vegetative state scale (CNPVSS) were evaluated before and after rTMS treatment, and the linear correlation between N13-N20, GCS, and CNPVSS was analysed. The scores of CNPVSS and GCS significantly increased in the first, second, and third months after rTMS. The N13-N20 was shorter in the second and third months after rTMS compared to before treatment. rTMS was found to shorten the N13-N20 latency, and there was a negative correlation between N13-N20 and the score of consciousness disorders. N13-N20 can serve as an objective index for evaluating consciousness disorders. This research provides potential insights for doctors in diagnosing patients with consciousness disorders.</p>\",\"PeriodicalId\":54735,\"journal\":{\"name\":\"Network-Computation in Neural Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Network-Computation in Neural Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1080/0954898X.2023.2269263\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/11/9 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Network-Computation in Neural Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1080/0954898X.2023.2269263","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/11/9 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
How somatosensory evoked potentials improve the diagnosis of the disturbance of consciousness: A retrospective analysis.
The interpeak latency is a crucial characteristic of upper limb somatosensory evoked potentials (USEPs). However, the existing research on the correlation between interpeak latency and consciousness disorders is currently limited. We aimed to investigate how USEPs can contribute to the diagnosis of consciousness disorders. A retrospective analysis was conducted on 10 patients who underwent repetitive transcranial magnetic stimulation (rTMS) for consciousness disorders. The interpeak latency N13-N20, Glasgow coma scale (GCS), and Chinese Nanjing persistent vegetative state scale (CNPVSS) were evaluated before and after rTMS treatment, and the linear correlation between N13-N20, GCS, and CNPVSS was analysed. The scores of CNPVSS and GCS significantly increased in the first, second, and third months after rTMS. The N13-N20 was shorter in the second and third months after rTMS compared to before treatment. rTMS was found to shorten the N13-N20 latency, and there was a negative correlation between N13-N20 and the score of consciousness disorders. N13-N20 can serve as an objective index for evaluating consciousness disorders. This research provides potential insights for doctors in diagnosing patients with consciousness disorders.
期刊介绍:
Network: Computation in Neural Systems welcomes submissions of research papers that integrate theoretical neuroscience with experimental data, emphasizing the utilization of cutting-edge technologies. We invite authors and researchers to contribute their work in the following areas:
Theoretical Neuroscience: This section encompasses neural network modeling approaches that elucidate brain function.
Neural Networks in Data Analysis and Pattern Recognition: We encourage submissions exploring the use of neural networks for data analysis and pattern recognition, including but not limited to image analysis and speech processing applications.
Neural Networks in Control Systems: This category encompasses the utilization of neural networks in control systems, including robotics, state estimation, fault detection, and diagnosis.
Analysis of Neurophysiological Data: We invite submissions focusing on the analysis of neurophysiology data obtained from experimental studies involving animals.
Analysis of Experimental Data on the Human Brain: This section includes papers analyzing experimental data from studies on the human brain, utilizing imaging techniques such as MRI, fMRI, EEG, and PET.
Neurobiological Foundations of Consciousness: We encourage submissions exploring the neural bases of consciousness in the brain and its simulation in machines.