Multi-physics modeling and finite-element formulation of neuronal dendrite growth with electrical polarization
Multi-physics modeling and finite-element formulation of neuronal dendrite growth with electrical polarization
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DOI：
10.1016/j.brain.2023.100071
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发表时间：
2023-05
影响因子：
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通讯作者：
Shuolun Wang;Xincheng Wang;Maria A. Holland
中科院分区：
文献类型：
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作者：
Shuolun Wang;Xincheng Wang;Maria A. Holland
神经元是大脑的基本计算单位。改变的神经元形态通常在各种神经系统疾病中发现，例如唐氏综合征、威廉姆斯综合征和特发性自闭症。令人信服的生物学证据表明，神经元的形态可以动态调节神经元的活动，通过介导的钙信号通路。此外，研究已经揭示，暴露于施加的电场可以诱导神经突朝向阴极的定向迁移。在这项研究中，我们开发了一个耦合系统，结合平流格雷斯科特模型与高斯定律，以更好地了解枝晶生长和电极化的反应。我们的模拟结果成功地捕捉到了关键的功能，如枝晶分支，空间填充，自我回避，和电极化。在卷积神经网络的帮助下，我们从一个在线的开放源代码逆向识别真实的枝晶形貌的模型参数。最后，我们校准了我们的模型，使用实验数据下施加electricalfields.Statement的意义：这项工作揭示了潜在的机制，通过数学建模和数值模拟下的电极化的神经元树突的生长。我们还使用机器学习技术来校准模型对真实的神经元图像。我们在线提供的数值实现和机器学习管道将有助于研究人员了解各种异常神经元形态和相关神经疾病的发展。
The neuron serves as the basic computational unit for the brain. Altered neuronal morphologies are usually found in various neurological diseases, such as Down syndrome, Williams syndrome, and idiopathic autism. Compelling biological evidence demonstrates that neuronal morphology can be dynamically regulated by neuronal activity through the mediation of calcium signaling pathways. Moreover, studies have revealed that exposure to an applied electric field can induce directional migration of neurites toward the cathode. In this study, we developed a coupled system that combines an advective Gray–Scott model with Gauss’s law to gain a better understanding of dendrite growth and response to electrical polarization. Our simulation results successfully capture key features such as dendrite branching, space-filling, self-avoidance, and electrical polarization. With the help of the convolutional neural network, we inversely identified model parameters of real dendrite morphologies from an online open source. Finally, we calibrated our model using experimental data on growing neurons under applied electric fields.Statement of Significance: The work sheds light on the underlying mechanisms that govern the growth of neuronal dendrites under electrical polarization via mathematical modeling and numerical simulations. We also use a machine-learning technique to calibrate the model against real neuron images. Our numerical implementations and machine-learning pipeline provided online would benefit researchers in understanding the development of various abnormal neuronal morphologies and related neurological diseases.