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在对外周神经进行人工刺激时,模拟电突触耦合对选择性和反应模式的影响。

Modelling the effects of ephaptic coupling on selectivity and response patterns during artificial stimulation of peripheral nerves.

机构信息

BCI-NE group, CSEE, University of Essex, Colchester, United Kingdom.

出版信息

PLoS Comput Biol. 2020 Jun 1;16(6):e1007826. doi: 10.1371/journal.pcbi.1007826. eCollection 2020 Jun.

DOI:10.1371/journal.pcbi.1007826
PMID:32479499
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7263584/
Abstract

Artificial electrical stimulation of peripheral nerves for sensory feedback restoration can greatly benefit from computational models for simulation-based neural implant design in order to reduce the trial-and-error approach usually taken, thus potentially significantly reducing research and development costs and time. To this end, we built a computational model of a peripheral nerve trunk in which the interstitial space between the fibers and the tissues was modelled using a resistor network, thus enabling distance-dependent ephaptic coupling between myelinated axons and between fascicles as well. We used the model to simulate a) the stimulation of a nerve trunk model with a cuff electrode, and b) the propagation of action potentials along the axons. Results were used to investigate the effect of ephaptic interactions on recruitment and selectivity stemming from artificial (i.e., neural implant) stimulation and on the relative timing between action potentials during propagation. Ephaptic coupling was found to increase the number of fibers that are activated by artificial stimulation, thus reducing the artificial currents required for axonal recruitment, and it was found to reduce and shift the range of optimal stimulation amplitudes for maximum inter-fascicular selectivity. During propagation, while fibers of similar diameters tended to lock their action potentials and reduce their conduction velocities, as expected from previous knowledge on bundles of identical axons, the presence of many other fibers of different diameters was found to make their interactions weaker and unstable.

摘要

人工电刺激周围神经以恢复感觉反馈,可以极大地受益于基于计算模型的神经植入物设计,以减少通常采用的反复试验方法,从而有可能显著降低研究和开发成本和时间。为此,我们构建了一个周围神经干的计算模型,其中纤维和组织之间的间质空间使用电阻器网络进行建模,从而能够在有髓轴突之间以及束之间实现距离依赖性的电突触耦合。我们使用该模型模拟了 a)袖套电极刺激神经干模型,以及 b)动作电位沿轴突的传播。结果用于研究电突触相互作用对源自人工(即神经植入物)刺激的募集和选择性的影响,以及传播过程中动作电位之间的相对定时。发现电突触耦合会增加被人工刺激激活的纤维数量,从而降低轴突募集所需的人工电流,并且发现它降低并改变了用于最大束间选择性的最佳刺激幅度范围。在传播过程中,虽然类似直径的纤维倾向于锁定其动作电位并降低其传导速度,这与先前关于相同轴突束的知识一致,但发现许多其他不同直径的纤维的存在使其相互作用减弱且不稳定。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f80/7263584/9093960ede08/pcbi.1007826.g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f80/7263584/172169d8beef/pcbi.1007826.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f80/7263584/590bda86bab6/pcbi.1007826.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f80/7263584/11935e2feefa/pcbi.1007826.g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f80/7263584/e99918897d65/pcbi.1007826.g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f80/7263584/9093960ede08/pcbi.1007826.g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f80/7263584/172169d8beef/pcbi.1007826.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f80/7263584/590bda86bab6/pcbi.1007826.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f80/7263584/11935e2feefa/pcbi.1007826.g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f80/7263584/e99918897d65/pcbi.1007826.g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f80/7263584/9093960ede08/pcbi.1007826.g013.jpg

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