PyPNS: Multiscale Simulation of a Peripheral Nerve in Python.
PyPNS: Multiscale Simulation of a Peripheral Nerve in Python.
复制标题
PYPN：Python中周围神经的多尺度模拟。
DOI：
10.1007/s12021-018-9383-z
复制
发表时间：
2019-01
期刊：
影响因子：
3
通讯作者：
Schultz SR
中科院分区：
文献类型：
--
作者：
Lubba CH;Le Guen Y;Jarvis S;Jones NS;Cork SC;Eftekhar A;Schultz SR
调节周围神经活动模式的生物电子药物有望成为治疗从癫痫到风湿病等多种疾病的新方法。该领域的进展建立在耗时和昂贵的活体实验之上。为了减少实验负荷并允许对周围神经刺激进行更快、更详细的分析和记录，结合实验见解的计算模型将大有帮助。我们提出了一个周围神经模拟器，结合了生物物理轴突模型和数值解决和理想的细胞外空间模型在一个环境中。我们将细胞外空间建模为一个三维电阻连续体，由麦克斯韦方程的准静态近似控制。在有限元模型中预先计算了不同介质（均匀介质、盐水中神经、袖带中神经）的电位分布，并将其输入到我们的模拟器中。另一方面，轴突被更抽象地建模为一维的隔室链。无髓鞘纤维是基于霍奇金-赫胥黎模型；对于髓鞘纤维，我们调整了McIntyre等人在2002年提出的模型，使其直径更小。为了获得真实的轴突形状，迭代算法沿神经定位纤维，具有可变的弯曲度，以适应成像轨迹。我们用受刺激的大鼠迷走神经的数据验证了我们的模型。仿真结果预测，扭曲改变了记录的信号形状，增加了刺激阈值。我们开发的模型可以很容易地适应不同的神经，并可能在未来的生物电子医学研究中使用。
Bioelectronic Medicines that modulate the activity patterns on peripheral nerves have promise as a new way of treating diverse medical conditions from epilepsy to rheumatism. Progress in the field builds upon time consuming and expensive experiments in living organisms. To reduce experimentation load and allow for a faster, more detailed analysis of peripheral nerve stimulation and recording, computational models incorporating experimental insights will be of great help. We present a peripheral nerve simulator that combines biophysical axon models and numerically solved and idealised extracellular space models in one environment. We modelled the extracellular space as a three-dimensional resistive continuum governed by the electro-quasistatic approximation of the Maxwell equations. Potential distributions were precomputed in finite element models for different media (homogeneous, nerve in saline, nerve in cuff) and imported into our simulator. Axons, on the other hand, were modelled more abstractly as one-dimensional chains of compartments. Unmyelinated fibres were based on the Hodgkin-Huxley model; for myelinated fibres, we adapted the model proposed by McIntyre et al. in 2002 to smaller diameters. To obtain realistic axon shapes, an iterative algorithm positioned fibres along the nerve with a variable tortuosity fit to imaged trajectories. We validated our model with data from the stimulated rat vagus nerve. Simulation results predicted that tortuosity alters recorded signal shapes and increases stimulation thresholds. The model we developed can easily be adapted to different nerves, and may be of use for Bioelectronic Medicine research in the future.
登录
查看更多内容
影响因子：
2.5
作者：
Goto, Takakuni;Hatanaka, Rieko;Kawashima, Ryuta
通讯作者：
Kawashima, Ryuta
DOI：
10.1007/bf01159386
发表时间：
1983-01-01
期刊：
JOURNAL OF NEUROCYTOLOGY
影响因子：
--
作者：
BERTHOLD, CH;RYDMARK, M
通讯作者：
RYDMARK, M
影响因子：
5.3
作者：
Bokil, H;Laaris, N;Keller, A
通讯作者：
Keller, A
影响因子：
1.2
作者：
Holt, GR;Koch, C
通讯作者：
Koch, C
影响因子：
3.7
作者：
Lempka SF;McIntyre CC
通讯作者：
McIntyre CC