小DRG神经元9D模型的计算分析。

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Journal of Computational Neuroscience Pub Date : 2020-11-01 Epub Date: 2020-08-30 DOI:10.1007/s10827-020-00761-6
Parul Verma, Achim Kienle, Dietrich Flockerzi, Doraiswami Ramkrishna
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引用次数: 5

摘要

小背根神经节(DRG)神经元是主要的痛觉感受器,负责感知疼痛。阐明它们的动态是理解和控制疼痛的必要条件。为此,本文对一个小DRG神经元模型进行了数值分岔分析。模型为霍奇金-赫胥黎型,有9个状态变量。它由Nav1.7和Nav1.8钠通道、泄漏通道、延迟整流钾通道和a型瞬态钾通道组成。该模型的动力学很大程度上取决于电压门控离子通道的最大电导和外部电流,它们可以通过实验调节。我们发现神经元动态对Nav1.8通道最大电导最为敏感(公式:见原文)。数值分岔分析表明,根据[公式:见文]和外部电流,可以识别出具有稳定稳态、动作电位周期放电、混合模式振荡(MMOs)以及稳定稳态和动作电位周期放电之间的双稳态的不同参数区域。我们说明和讨论这些不同制度之间的过渡。我们进一步分析mmo游戏的行为。当外加电流减小时,我们发现mmo出现在一个循环极限点之后。在这个区域内,分岔分析显示了一系列孤立的周期解分支,每个周期具有一个大动作电位和许多小振幅峰。随着外部电流的减小,小振幅峰的数量增加,大振幅动作电位之间的距离增大,最终趋于无穷大,从而达到稳定的稳态。仔细观察就会发现在这些周期性MMO分支之间有更复杂的连接MMO,形成了Farey序列。最后,我们还发现了具有看似混沌的非周期振荡的小解窗。这里发现的动态模式——作为由不同参数调节的分岔点的结果——具有潜在的翻译意义,因为动作电位的重复放电意味着某种形式和强度的疼痛;通过调节不同的参数来操纵这些模式有助于研究疼痛动力学。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computational analysis of a 9D model for a small DRG neuron.

Small dorsal root ganglion (DRG) neurons are primary nociceptors which are responsible for sensing pain. Elucidation of their dynamics is essential for understanding and controlling pain. To this end, we present a numerical bifurcation analysis of a small DRG neuron model in this paper. The model is of Hodgkin-Huxley type and has 9 state variables. It consists of a Nav1.7 and a Nav1.8 sodium channel, a leak channel, a delayed rectifier potassium, and an A-type transient potassium channel. The dynamics of this model strongly depend on the maximal conductances of the voltage-gated ion channels and the external current, which can be adjusted experimentally. We show that the neuron dynamics are most sensitive to the Nav1.8 channel maximal conductance ([Formula: see text]). Numerical bifurcation analysis shows that depending on [Formula: see text] and the external current, different parameter regions can be identified with stable steady states, periodic firing of action potentials, mixed-mode oscillations (MMOs), and bistability between stable steady states and stable periodic firing of action potentials. We illustrate and discuss the transitions between these different regimes. We further analyze the behavior of MMOs. As the external current is decreased, we find that MMOs appear after a cyclic limit point. Within this region, bifurcation analysis shows a sequence of isolated periodic solution branches with one large action potential and a number of small amplitude peaks per period. For decreasing external current, the number of small amplitude peaks is increasing and the distance between the large amplitude action potentials is growing, finally tending to infinity and thereby leading to a stable steady state. A closer inspection reveals more complex concatenated MMOs in between these periodic MMO branches, forming Farey sequences. Lastly, we also find small solution windows with aperiodic oscillations which seem to be chaotic. The dynamical patterns found here-as consequences of bifurcation points regulated by different parameters-have potential translational significance as repetitive firing of action potentials imply pain of some form and intensity; manipulating these patterns by regulating the different parameters could aid in investigating pain dynamics.

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来源期刊
CiteScore
2.00
自引率
8.30%
发文量
32
审稿时长
3 months
期刊介绍: The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods yielding insights into the function of the nervous system, are also welcomed (in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields).It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience.
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