Institute of Continuum Mechanics and Biomechanics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058, Erlangen, Germany.
Department of Computing, Imperial College London, London, SW7 2AZ, UK.
Sci Rep. 2024 Oct 30;14(1):26103. doi: 10.1038/s41598-024-75952-7.
The human brain's distinctive folding pattern has attracted the attention of researchers from different fields. Neuroscientists have provided insights into the role of four fundamental cell types crucial during embryonic development: radial glial cells, intermediate progenitor cells, outer radial glial cells, and neurons. Understanding the mechanisms by which these cell types influence the number of cortical neurons and the emerging cortical folding pattern necessitates accounting for the mechanical forces that drive the cortical folding process. Our research aims to explore the correlation between biological processes and mechanical forces through computational modeling. We introduce cell-density fields, characterized by a system of advection-diffusion equations, designed to replicate the characteristic behaviors of various cell types in the developing brain. Concurrently, we adopt the theory of finite growth to describe cortex expansion driven by increasing cell density. Our model serves as an adjustable tool for understanding how the behavior of individual cell types reflects normal and abnormal folding patterns. Through comparison with magnetic resonance images of the fetal brain, we explore the correlation between morphological changes and underlying cellular mechanisms. Moreover, our model sheds light on the spatiotemporal relationships among different cell types in the human brain and enables cellular deconvolution of histological sections.
人脑独特的折叠模式引起了不同领域研究人员的关注。神经科学家深入研究了在胚胎发育过程中起关键作用的四种基本细胞类型:放射状胶质细胞、中间祖细胞、外放射状胶质细胞和神经元。为了了解这些细胞类型如何影响皮质神经元的数量和皮质折叠模式的出现,需要考虑驱动皮质折叠过程的机械力。我们的研究旨在通过计算建模探索生物学过程和机械力之间的相关性。我们引入了细胞密度场,它由一组平流-扩散方程描述,旨在复制发育中大脑中各种细胞类型的特征行为。同时,我们采用有限生长理论来描述由细胞密度增加引起的皮质扩张。我们的模型是一个可调节的工具,用于了解单个细胞类型的行为如何反映正常和异常折叠模式。通过与胎儿大脑的磁共振成像进行比较,我们探索了形态变化与潜在细胞机制之间的相关性。此外,我们的模型揭示了人脑不同细胞类型之间的时空关系,并能够对组织学切片进行细胞分解。