In this work, we introduce a novel computational framework that we developed to use numerical simulations to investigate the complexity of brain tissue at a microscopic level with a detail never realised before. Directly inspired by the advances in computational neuroscience for modelling brain cells, we propose a generative model that enables us to simulate molecular diffusion within realistic digitalised brain cells, such as neurons and glia, in a completely controlled and flexible fashion. We validate our new approach by showing an excellent match between the morphology and simulated DW-MR signal of the generated digital model of brain cells and those of digital reconstruction of real brain cells from available open-access databases. We demonstrate the versatility and potentiality of the framework by showing a select set of examples of relevance for the DW-MR community. Further development is ongoing, which will support even more realistic conditions like dense packing of numerous 3D complex cell structures and varying cell surface permeability.
翻译:在这项工作中,我们引入了一个新型的计算框架,我们开发了这个框架,以利用数字模拟来调查微小层大脑组织的复杂性,其细节从未实现过。直接受模拟脑细胞的计算神经科学进步的启发,我们提出了一个基因模型,使我们能够以完全控制和灵活的方式模拟分子在现实的数字化脑细胞(如神经元和Glia)中的扩散。我们验证了我们的新方法,在生成的脑细胞数字模型的形态学和模拟DW-MR信号与现有开放访问数据库中真实脑细胞数字重建信号的形态学和模拟DW-MR信号之间表现出了极好的匹配。我们展示了框架的多功能性和潜力,展示了一组与DW-MR社区相关的特定例子。进一步的开发将支持更为现实的条件,比如大量3D复杂细胞结构的密集包装和不同的细胞表面渗透性。