Abstract

Purpose

Non-invasive estimation of the conduction velocity distribution (CVD) of a peripheral nerve has the potential to both improve clinical diagnoses of pathology and to observe the progress of the disease or the efficacy of treatments. Comparisons were made of the performance of three non-invasive CVD estimation methods proposed by independent research groups on peripheral nerve bundles under different conditions.

Methods

The first method (Cummins) uses a nerve compound action potential (CAP) with temporal dispersion and a mathematical single fiber action potential (SFAP). The second method (Barker) uses two CAPs and a non-mathematical SFAP waveform. The third method (Hirose) uses two CAPs recorded from distal and proximal sites. The Cummins and Barker methods have iterative solutions in the time domain while the Hirose method is a deconvolution estimator in the frequency domain. In order to compare these methods, we used cold exposure to affect primarily motor fibers and ischemia to affect primarily sensory fibers on rat caudal nerve bundles.

Results

The Cummins method is sensitive to changes in motor and sensory fiber percentages in CVD if it is used with the volume conductor model. The Barker and Hirose methods are sensitive to motor fiber percentages in CVD but they cannot detect changes in sensory fiber percentages accurately.

Conclusions

Estimation of the CVD using a priori SFAP created with a volume conductor model can non-invasively supply accurate and precise information about fiber groups in a peripheral nerve bundle.

中文翻译：

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冷暴露和缺血的神经传导速度分布方法比较

摘要

目的

外周神经传导速度分布 (CVD) 的非侵入性估计具有改善病理学临床诊断和观察疾病进展或治疗效果的潜力。比较了独立研究小组提出的三种无创CVD估计方法在不同条件下对周围神经束的性能。

方法

第一种方法 (Cummins) 使用具有时间离散度的神经复合动作电位 (CAP) 和数学单纤维动作电位 (SFAP)。第二种方法（Barker）使用两个 CAP 和一个非数学 SFAP 波形。第三种方法（Hirose）使用从远端和近端记录的两个 CAP。Cummins 和 Barker 方法在时域中具有迭代解，而 Hirose 方法是频域中的反卷积估计器。为了比较这些方法，我们使用冷暴露主要影响运动纤维和缺血主要影响大鼠尾神经束上的感觉纤维。

结果

如果与体积导体模型一起使用，康明斯方法对 CVD 中运动和感觉纤维百分比的变化很敏感。Barker 和 Hirose 方法对 CVD 中的运动纤维百分比敏感，但不能准确检测感觉纤维百分比的变化。

结论

使用体积导体模型创建的先验 SFAP 估计 CVD 可以无创地提供有关周围神经束中纤维组的准确和精确的信息。