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How electrically evoked compound action potentials in chronically implanted guinea pigs relate to auditory nerve health and electrode impedance
The Journal of the Acoustical Society of America ( IF 2.1 ) Pub Date : 2020-12-22 , DOI: 10.1121/10.0002882
Kara C Schvartz-Leyzac 1 , Deborah J Colesa 1 , Christopher J Buswinka 1 , Andrew M Rabah 1 , Donald L Swiderski 1 , Yehoash Raphael 1 , Bryan E Pfingst 1
Affiliation  

This study examined how multiple measures based on the electrically evoked compound action potential (ECAP) amplitude-growth functions (AGFs) were related to estimates of neural [spiral ganglion neuron (SGN) density and cell size] and electrode impedance measures in 34 specific pathogen free pigmented guinea pigs that were chronically implanted (4.9–15.4 months) with a cochlear implant electrode array. Two interphase gaps (IPGs) were used for the biphasic pulses and the effect of the IPG on each ECAP measure was measured (“IPG effect”). When using a stimulus with a constant IPG, SGN density was related to the across-subject variance in ECAP AGF linear slope, peak amplitude, and N1 latency. The SGN density values also help to explain a significant proportion of variance in the IPG effect for AGF linear slope and peak amplitude measures. Regression modeling revealed that SGN density was the primary dependent variable contributing to across-subject variance for ECAP measures; SGN cell size did not significantly improve the fitting of the model. Results showed that simple impedance measures were weakly related to most ECAP measures but did not typically improve the fit of the regression model.

中文翻译:

长期植入豚鼠的电诱发复合动作电位与听觉神经健康和电极阻抗的关系

本研究检查了基于电诱发复合动作电位 (ECAP) 幅度增长函数 (AGF) 的多项测量值与神经 [螺旋神经节神经元 (SGN) 密度和细胞大小] 的估计值和 34 种特定病原体的电极阻抗测量值之间的关系长期植入人工耳蜗电极阵列(4.9-15.4 个月)的游离色素豚鼠。两个相间间隙 (IPG) 用于双相脉冲,并测量了 IPG 对每个 ECAP 测量的影响(“IPG 效应”)。当使用具有恒定 IPG 的刺激时,SGN 密度与 ECAP AGF 线性斜率、峰值幅度和 N1 潜伏期的跨受试者差异有关。SGN 密度值还有助于解释 AGF 线性斜率和峰值幅度测量值的 IPG 效应方差的显着比例。回归模型显示,SGN 密度是导致 ECAP 措施跨学科差异的主要因变量;SGN 单元格大小并没有显着提高模型的拟合度。结果表明,简单的阻抗测量与大多数 ECAP 测量的相关性较弱,但通常不会改善回归模型的拟合。
更新日期:2020-12-23
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