Department of Neurology, University of Minnesota, Minneapolis, MN, USA.
Department of Neurology, University of Minnesota, Minneapolis, MN, USA.
J Neurosci Methods. 2022 Jan 1;365:109377. doi: 10.1016/j.jneumeth.2021.109377. Epub 2021 Oct 8.
In clinical practice, small myelinated sensory fibers conveying pain and other sensations, Aδ-fibers, cannot be examined with available nerve conduction study techniques.
Equipment available in clinical neurophysiology laboratories is used to record from human sensory nerves multiple averaged responses to non-painful stimulation of intraepidermal nerves. Ten averaged responses are analyzed in all possible pair combinations with an algorithm applied to a 0.45 ms period of amplitude and frequency (power spectrum). The median of the algorithms is compared to control data to identify potentials generated as response to intraepidermal stimulation.
Median analysis of the algorithm applied to amplitude and frequency of multiple record pairs identifies potentials with conduction velocities of Aδ-fibers. The analysis of frequency (power spectrum) adds data to the analysis of amplitude. Median analysis of multiple record pairs yields more data than analysis of one pair of alternate averages with the same algorithms.
COMPARISON WITH EXISTING METHOD(S): At present, analysis of one pair of alternate average records with an algorithm is the only method to identify Aδ-fiber generated potentials. Median analysis of the same algorithm applied to the amplitude of multiple record pairs increases the number of Aδ-fiber generated potentials identified. Neither median analysis of amplitude nor frequency of multiple records pairs has ever been used for conduction studies of nerve fibers, including Aδ-fibers.
Stimulation, recording and data analysis methods used in this study can be applied in the clinical EMG laboratory to identify the conduction velocities of Aδ-fibers in human sensory nerves.
在临床实践中,无法使用现有的神经传导研究技术来检查传递疼痛和其他感觉的小髓鞘感觉纤维,即 Aδ 纤维。
使用临床神经生理学实验室中现有的设备,从人类感觉神经记录对表皮内神经进行无痛刺激的多次平均反应。用算法分析所有可能的对组合,对 0.45 毫秒的幅度和频率(功率谱)进行 10 次平均响应。将算法的中位数与对照数据进行比较,以识别对表皮内刺激产生的潜在反应。
应用于幅度和频率的算法的中位数分析可识别具有 Aδ 纤维传导速度的潜在反应。频率(功率谱)的分析增加了幅度分析的数据。应用相同算法对多个记录对的中位数分析比用同一算法对一对交替平均值的分析产生更多的数据。
目前,用算法分析一对交替平均记录是识别 Aδ 纤维产生的潜在反应的唯一方法。应用相同算法对多个记录对幅度的中位数分析增加了识别的 Aδ 纤维产生的潜在反应数量。多记录对的幅度和频率的中位数分析均未用于包括 Aδ 纤维在内的神经纤维的传导研究。
本研究中使用的刺激、记录和数据分析方法可应用于临床肌电图实验室,以确定人类感觉神经中 Aδ 纤维的传导速度。