How somatosensory evoked potentials improve the diagnosis of the disturbance of consciousness: A retrospective analysis.

IF 1.1 3区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Network-Computation in Neural Systems Pub Date : 2023-02-01 Epub Date: 2023-11-09 DOI:10.1080/0954898X.2023.2269263

Xinwei Wang, Hongliang Gao, Jiulong Song, Peng Jing, Chao Wang, Nuanxin Yu, Shanshan Wu, Jianxiong Zhu, Zhiqiang Gao

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Abstract

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.

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体感诱发电位如何改善意识障碍的诊断：回顾性分析。

峰间潜伏期是上肢体感诱发电位的一个重要特征。然而，目前关于间歇潜伏期与意识障碍之间相关性的研究有限。我们的目的是研究USEP如何有助于意识障碍的诊断。对10例因意识障碍接受重复经颅磁刺激（rTMS）的患者进行了回顾性分析。评估rTMS治疗前后的潜伏期N13-N20、格拉斯哥昏迷量表（GCS）和中国南京持续性植物状态量表（CNPVSS），并分析N13-N20GCS和CNPVSS之间的线性相关性。在rTMS后的第一、第二和第三个月，CNPVSS和GCS评分显著增加。与治疗前相比，rTMS后第二个月和第三个月的N13-N20更短。rTMS可缩短N13-N20潜伏期，N13-N22 0与意识障碍评分呈负相关。N13-N20可以作为评价意识障碍的客观指标。这项研究为医生诊断意识障碍患者提供了潜在的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Network-Computation in Neural Systems 工程技术-工程：电子与电气

CiteScore

3.70

自引率

1.30%

发文量

审稿时长

>12 weeks

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.