Translational Neuropsychiatry Unit, Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 11, 8200 Aarhus N, Denmark.
Department of Biomedicine - Forskning og uddannelse, Vest, Aarhus University, Vest Ole Worms Allé 4 Bygning 1160, lokale 229, 8000 Aarhus C, Denmark.
Brief Bioinform. 2024 May 23;25(4). doi: 10.1093/bib/bbae265.
The development of the human central nervous system initiates in the early embryonic period until long after delivery. It has been shown that several neurological and neuropsychiatric diseases originate from prenatal incidents. Mathematical models offer a direct way to understand neurodevelopmental processes better. Mathematical modelling of neurodevelopment during the embryonic period is challenging in terms of how to 'Approach', how to initiate modelling and how to propose the appropriate equations that fit the underlying dynamics of neurodevelopment during the embryonic period while including the variety of elements that are built-in naturally during the process of neurodevelopment. It is imperative to answer where and how to start modelling; in other words, what is the appropriate 'Approach'? Therefore, one objective of this study was to tackle the mathematical issue broadly from different aspects and approaches. The approaches were divided into three embryonic categories: cell division, neural tube growth and neural plate growth. We concluded that the neural plate growth approach provides a suitable platform for simulation of brain formation/neurodevelopment compared to cell division and neural tube growth. We devised a novel equation and designed algorithms that include geometrical and topological algorithms that could fit most of the necessary elements of the neurodevelopmental process during the embryonic period. Hence, the proposed equations and defined mathematical structure would be a platform to generate an artificial neural network that autonomously grows and develops.
人类中枢神经系统的发育始于胚胎早期,一直持续到分娩后很久。已经表明,一些神经和神经精神疾病起源于产前事件。数学模型为更好地理解神经发育过程提供了一种直接的方法。胚胎期神经发育的数学建模在如何“接近”、如何启动建模以及如何提出适合胚胎期神经发育潜在动力学的适当方程方面具有挑战性,同时包括在神经发育过程中自然内置的各种元素。至关重要的是要回答在哪里以及如何开始建模;换句话说,适当的“方法”是什么?因此,本研究的一个目标是从不同的方面和方法广泛解决数学问题。这些方法分为三个胚胎期类别:细胞分裂、神经管生长和神经板生长。我们得出的结论是,与细胞分裂和神经管生长相比,神经板生长方法为模拟大脑形成/神经发育提供了一个合适的平台。我们设计了一个新的方程,并设计了包括几何和拓扑算法的算法,这些算法可以适应胚胎期神经发育过程中大多数必要的元素。因此,所提出的方程和定义的数学结构将成为一个生成自主生长和发育的人工神经网络的平台。