Modelling Human Brain Development

人脑的神经网络可以说是最复杂的生物模式；然而，形成这种神经系统的机制尚不清楚。神经系统在拓扑结构上表现出涌现特性，在信息处理方面表现出功能涌现特性。结构属性是模块化和层次化连接、远距离连接和高度连接节点的兴起。功能属性是信息的分布和集成、处理不同任务的专门模块的形成以及由于并行处理而产生的快速反应时间。神经成像的最新进展，使用扩散张量成像，使我们能够观察到人类大脑网络如何随着年龄的不同而不同，从胚胎阶段到成年阶段(20岁)。该项目将通过将数据分析与大脑发育模拟相结合，分析人脑网络是如何在发育过程中出现的。目的是开发人脑发育的模拟，分析人脑在不同发育阶段的网络特征，并将模拟与真实数据进行比较，以发现脑网络发展的潜在机制。模拟对于研究不同发育参数对最终大脑网络以及发育过程中的中间网络的作用至关重要。了解这些参数如何导致(成人)网络特征，将有助于评估这些参数对健康和病理发育的贡献。一旦了解了发展的时间进程，我们也应该能够预测未来发展阶段的概率。这对于预测发育性疾病的进展是至关重要的。除了了解人类认知系统的形成，这些结果还将为人工信息处理系统的设计和更新提供信息。识别这些关键的发育机制将极大地提高我们对生物系统中涌现的理解。此外，这可能导致几个关于精神分裂症、癫痫和自闭症等大脑疾病上升的预测，这些疾病通常起源于发育期间，并与中枢组织的变化有关。除了生物模式的形成，这种用于人脑发育的“算法”可以告诉我们如何构建人工智能系统。与其以自上而下的方式构建人工大脑，不如将已确定的神经网络发展机制应用于智能信息处理系统的出现，从而产生适应能力更强的系统。总而言之，人脑的形成是一个涉及到自然和人工信息处理系统的出现的根本问题。

The neural network of the human brain is arguably the most complex biological pattern; however, the mechanisms forming such neural systems are unclear. Neural systems show structurally emergent properties in terms of their topology and functionally emergent properties concerning information processing. Structural properties are the rise of modular and hierarchical connectivity, of long-distance connections, and of highly connected nodes. Functional properties are the distribution and integration of information, the formation of specialized modules dealing with different tasks, and a rapid reaction time due to parallel processing. Recent advances in neuroimaging, using diffusion tensor imaging, allow us to observe how the human brain network differs over ages ranging from the embryonic to the adult stage (age of 20 years). This project will analyse how the human brain network arises during development by combining data analysis with simulations of brain development. Objectives are to develop a simulation of human brain development, to analyse network features of human brains at different developmental stages, and to compare simulations with real data to discover the underlying mechanisms for brain network development. Simulations are crucial to study the role of different developmental parameters on the final brain network as well as on intermediate networks during development. Understanding how parameters lead to (adult) network features will help to evaluate the contribution of these parameters to healthy and pathological development. Once understanding the time course of development, we should also be able to predict the probabilities of future stages of development. This will be crucial for giving a prognosis for the progression of developmental diseases. In addition to understanding the formation of human cognitive systems, these results will inform the design and update of artificial information processing systems. Identifying these key developmental mechanisms will greatly improve our understanding of emergence in biological systems. In addition, it might lead to several predictions about the rise of brain disorders such as schizophrenia, epilepsy, and autism that often originate during development and are linked to changes in hub organization. Beyond biological pattern formation, such 'algorithms' for human brain development could inform us how to build artificial intelligent systems. Rather than constructing artificial brains in a top-down manner, applying identified mechanisms for neural network development will allow the emergence of intelligent information processing systems leading to systems that are more adaptable. In summary, the formation of human brains is a fundamental question that touches on the emergence of natural and man-made information processing systems.