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用于无创颈迷走神经刺激的高分辨率多尺度计算模型

High-Resolution Multi-Scale Computational Model for Non-Invasive Cervical Vagus Nerve Stimulation.

作者信息

Mourdoukoutas Antonios P, Truong Dennis Q, Adair Devin K, Simon Bruce J, Bikson Marom

机构信息

Department of Biomedical Engineering, The City College of New York, City University of New York, New York, NY, USA.

Department of Psychology, The Graduate Center, City University of New York, New York, New York, USA.

出版信息

Neuromodulation. 2018 Apr;21(3):261-268. doi: 10.1111/ner.12706. Epub 2017 Oct 27.

Abstract

OBJECTIVES

To develop the first high-resolution, multi-scale model of cervical non-invasive vagus nerve stimulation (nVNS) and to predict vagus fiber type activation, given clinically relevant rheobase thresholds.

METHODS

An MRI-derived Finite Element Method (FEM) model was developed to accurately simulate key macroscopic (e.g., skin, soft tissue, muscle) and mesoscopic (cervical enlargement, vertebral arch and foramen, cerebral spinal fluid [CSF], nerve sheath) tissue components to predict extracellular potential, electric field (E-Field), and activating function along the vagus nerve. Microscopic scale biophysical models of axons were developed to compare axons of varying size (Aα-, Aβ- and Aδ-, B-, and C-fibers). Rheobase threshold estimates were based on a step function waveform.

RESULTS

Macro-scale accuracy was found to determine E-Field magnitudes around the vagus nerve, while meso-scale precision determined E-field changes (activating function). Mesoscopic anatomical details that capture vagus nerve passage through a changing tissue environment (e.g., bone to soft tissue) profoundly enhanced predicted axon sensitivity while encapsulation in homogenous tissue (e.g., nerve sheath) dulled axon sensitivity to nVNS.

CONCLUSIONS

These findings indicate that realistic and precise modeling at both macroscopic and mesoscopic scales are needed for quantitative predictions of vagus nerve activation. Based on this approach, we predict conventional cervical nVNS protocols can activate A- and B- but not C-fibers. Our state-of-the-art implementation across scales is equally valuable for models of spinal cord stimulation, cortex/deep brain stimulation, and other peripheral/cranial nerve models.

摘要

目的

开发首个高分辨率、多尺度的颈部非侵入性迷走神经刺激(nVNS)模型,并根据临床相关基强度阈值预测迷走神经纤维类型的激活情况。

方法

开发了一种基于磁共振成像(MRI)的有限元方法(FEM)模型,以精确模拟关键的宏观(如皮肤、软组织、肌肉)和介观(颈膨大、椎弓和椎间孔、脑脊液[CSF]、神经鞘)组织成分,从而预测迷走神经周围的细胞外电位、电场(E场)和激活函数。建立了轴突的微观尺度生物物理模型,以比较不同大小的轴突(Aα、Aβ和Aδ、B和C纤维)。基强度阈值估计基于阶跃函数波形。

结果

发现宏观尺度的准确性决定了迷走神经周围的E场大小,而介观尺度的精度决定了E场变化(激活函数)。捕捉迷走神经穿过变化的组织环境(如从骨到软组织)的介观解剖细节显著提高了预测的轴突敏感性,而包裹在均匀组织(如神经鞘)中则降低了轴突对nVNS的敏感性。

结论

这些发现表明,为了对迷走神经激活进行定量预测,需要在宏观和介观尺度上进行真实而精确的建模。基于此方法,我们预测传统的颈部nVNS方案可激活A和B纤维,但不能激活C纤维。我们在各尺度上的先进实现对于脊髓刺激、皮层/深部脑刺激以及其他外周/颅神经模型同样具有价值。

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