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具有周期性几何图案的基底上神经元动力学的偏置随机游走模型。

Biased Random Walk Model of Neuronal Dynamics on Substrates with Periodic Geometrical Patterns.

作者信息

Staii Cristian

机构信息

Department of Physics and Astronomy, Tufts University, Medford, MA 02155, USA.

出版信息

Biomimetics (Basel). 2023 Jun 20;8(2):267. doi: 10.3390/biomimetics8020267.

Abstract

Neuronal networks are complex systems of interconnected neurons responsible for transmitting and processing information throughout the nervous system. The building blocks of neuronal networks consist of individual neurons, specialized cells that receive, process, and transmit electrical and chemical signals throughout the body. The formation of neuronal networks in the developing nervous system is a process of fundamental importance for understanding brain activity, including perception, memory, and cognition. To form networks, neuronal cells extend long processes called axons, which navigate toward other target neurons guided by both intrinsic and extrinsic factors, including genetic programming, chemical signaling, intercellular interactions, and mechanical and geometrical cues. Despite important recent advances, the basic mechanisms underlying collective neuron behavior and the formation of functional neuronal networks are not entirely understood. In this paper, we present a combined experimental and theoretical analysis of neuronal growth on surfaces with micropatterned periodic geometrical features. We demonstrate that the extension of axons on these surfaces is described by a biased random walk model, in which the surface geometry imparts a constant drift term to the axon, and the stochastic cues produce a random walk around the average growth direction. We show that the model predicts key parameters that describe axonal dynamics: diffusion (cell motility) coefficient, average growth velocity, and axonal mean squared length, and we compare these parameters with the results of experimental measurements. Our findings indicate that neuronal growth is governed by a contact-guidance mechanism, in which the axons respond to external geometrical cues by aligning their motion along the surface micropatterns. These results have a significant impact on developing novel neural network models, as well as biomimetic substrates, to stimulate nerve regeneration and repair after injury.

摘要

神经网络是由相互连接的神经元组成的复杂系统,负责在整个神经系统中传递和处理信息。神经网络的基本组成部分是单个神经元,这些特殊的细胞在全身接收、处理和传递电信号和化学信号。发育中的神经系统中神经网络的形成是一个对于理解大脑活动(包括感知、记忆和认知)至关重要的过程。为了形成网络,神经元细胞会延伸出称为轴突的长突起,这些轴突在内在和外在因素(包括基因编程、化学信号传导、细胞间相互作用以及机械和几何线索)的引导下朝着其他目标神经元移动。尽管最近取得了重要进展,但集体神经元行为和功能性神经网络形成的基本机制仍未完全被理解。在本文中,我们对具有微图案化周期性几何特征的表面上的神经元生长进行了实验和理论相结合的分析。我们证明,这些表面上轴突的延伸可以用有偏随机游走模型来描述,其中表面几何形状给轴突赋予一个恒定的漂移项,而随机线索在平均生长方向周围产生随机游走。我们表明,该模型预测了描述轴突动力学的关键参数:扩散(细胞运动)系数、平均生长速度和轴突均方长度,并且我们将这些参数与实验测量结果进行了比较。我们的研究结果表明,神经元生长受接触导向机制支配,其中轴突通过使其运动沿着表面微图案排列来响应外部几何线索。这些结果对开发新型神经网络模型以及仿生基质以刺激损伤后的神经再生和修复具有重大影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac9/10296521/8b64406c403e/biomimetics-08-00267-g001.jpg

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