arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2001.10414q-bio.NC
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在皮层组织的详细计算模型中预测电场刺激的影响

Predicting the Impact of Electric Field Stimulation in a Detailed Computational Model of Cortical Tissue

Frances Hutchings, Christopher Thornton, Chencheng Zhang, Yujiang Wang, Marcus Kaiser

摘要:

近年来,使用弱电场的神经刺激由于其作为医疗干预的潜力而引起了人们的兴奋。然而,这种刺激方式的研究受到了不一致的结果和研究内和研究之间的大变异性的阻碍。为了开始解决这种可变性,我们需要适当地考虑电流对底层神经元群体的影响。开发和测试能够捕获电场刺激对神经元网络的影响的计算模型。我们构建了一个具有不同层次和明确的神经元形态的皮层组织模型。然后,我们应用电刺激模型,并进行多个测试用例模拟。皮层切片模型进行了比较,实验文献和显示捕捉的电生理刺激反应的主要特征。也就是说,该模型显示:1)单个锥体神经元中类似水平的去极化,2)内在振荡的加速,以及3)不同层中振荡的空间分布的保留。然后,我们应用替代电场,以证明该模型可以捕捉神经元响应的电场的差异。我们表明,组织反应是依赖于层的深度,相对于该领域的顶端树突的角度,和刺激强度。我们提出了公开可用的计算建模软件,预测的神经元网络人口对电场刺激的反应。

英文摘要:

Neurostimulation using weak electric fields has generated excitement in recent years due to its potential as a medical intervention. However, study of this stimulation modality has been hampered by inconsistent results and large variability within and between studies. In order to begin addressing this variability we need to properly characterise the impact of the current on the underlying neuron populations. To develop and test a computational model capable of capturing the impact of electric field stimulation on networks of neurons. We construct a cortical tissue model with distinct layers and explicit neuron morphologies. We then apply a model of electrical stimulation and carry out multiple test case simulations. The cortical slice model is compared to experimental literature and shown to capture the main features of the electrophysiological response to stimulation. Namely, the model showed 1) a similar level of depolarisation in individual pyramidal neurons, 2) acceleration of intrinsic oscillations, and 3) retention of the spatial profile of oscillations in different layers. We then apply alternative electric fields to demonstrate how the model can capture differences in neuronal responses to the electric field. We demonstrate that the tissue response is dependent on layer depth, the angle of the apical dendrite relative to the field, and stimulation strength. We present publicly available computational modelling software that predicts the neuron network population response to electric field stimulation.

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