Modelling the Development of Complex Brain Networks

网络无处不在。在自然界、社会、技术和商业中，分析各个组成部分之间的联系模式通常是有用的。例如，这种方法可以用于识别社交网络中的关键参与者，或者确保电网的稳健性，以应对目标攻击和随机故障。大脑也可以看作是一个巨大的网络。各种脑成像技术可用于识别各个大脑区域之间的联系，无论是从解剖学的角度还是从信息流的角度（有关视频说明，请访问：http://www.youtube.com/watch?v=f3P15X_62xQ）。在过去的5年里，科学家们越来越多地研究大脑区域之间的这种连接模式，以更好地了解整个大脑。例如，他们发现这些网络的某些特性与更高的智商相关。另一个令人惊讶的发现是，许多不同类型的网络，从人类大脑到证券交易所，都有大量的共同属性。最后，这种新方法使研究人员能够识别不同人群之间大脑网络结构的差异。例如，众所周知，年轻人和老年人的大脑网络具有不同的特性。在精神分裂症等精神健康障碍患者中发现了大脑网络的其他变化。描述大脑网络组织中的这种差异可能对精神疾病的诊断变得重要。然而，为了更好地治疗和预防，我们还需要了解这些差异是如何产生的。目前，大多数数学方法都是为静态网络的分析而设计的，“冻结在时间里”。该项目旨在开发新的工具来模拟大脑网络随着时间的发展，并了解这些变化背后的驱动力。这些新方法将被应用于研究人类大脑发育的两个关键时期：青春期和衰老期。为此，我们将使用认知测试以及先前从14至88岁的参与者中收集的高质量脑成像数据。我们将重点关注以下几个问题：在描述大脑组织在发育和衰老过程中发生的成熟变化时，哪些网络特征最有用？为了解决这个问题，我们将使用新的和已有的网络结构测量方法。大脑网络的哪些特征与年轻人，尤其是老年人更好的认知表现有关？年轻时网络组织的某些特征是否可以预测后期的认知能力？在量化了大脑网络在人类大脑发育过程中的变化之后，我们可以开始寻找控制这些变化的规则。例如，我们能否建立简单的模型来预测衰老过程中组织损失的模式？随着越来越多的人寿命越来越长，这个问题显得尤为重要。这项研究将在剑桥大学的大脑测绘部门（BMU）进行。BMU由Ed Bullmore教授领导，汇集了医学、物理、数学等不同背景的研究人员。这种独特的组合为该项目提供了一系列关键领域的世界级专业知识，并为开展拟议的工作提供了完美的环境。

Networks are everywhere around us. In nature, society, technology and commerce it is often useful to analyze the patterns of connections between individual components. This approach can, for example, be used to identify key players in a social network or to ensure the robustness of a power-grid to both targeted attacks and random failures. The brain too can be viewed as a large network. Various brain imaging techniques can be used to identify the links between individual brain regions, either in terms of anatomical connection or in terms of the flow of information (For a video illustration, visit: http://www.youtube.com/watch?v=f3P15X_62xQ). Over the last 5 years, scientists have increasingly studied this pattern of connections between brain regions to gain a better understanding of the brain as a whole. For example, they have found that certain properties of these networks are correlated with higher IQ. Another surprising finding was that many different types of networks, from the human brain to the stock exchange have a large number of properties in common. Finally, this new approach has allowed researchers to identify differences in the structure of brain networks between different populations. For example, brain networks are known to have somewhat different properties in young and older people. Other alterations of brain networks were identified in people with mental health disorders such as schizophrenia. Describing such differences in the organization of brain networks is likely to become important in the diagnosis of mental illness. However, in order to lead to better treatment and prevention, we also need to understand how these differences come about. Currently, most mathematical methods are designed for the analysis of static networks, 'frozen in time'. This project aims to develop new tools to model the development of brain networks over time and to understand the driving forces behind these changes. These new methods will then be applied to the study of two key periods of human brain development: adolescence and ageing. For this, we will be using cognitive tests as well as high-quality brain imaging data previously collected from participants ranging from 14 to 88 years of age. We will focus, in particular, on the following questions:1. What network features are most useful in describing the maturational changes in brain organization taking place during development and ageing? In addressing this question, we will be using both novel and pre-existing measures of network structure.2. What characteristics of brain networks are associated with better cognitive performance in youth, and especially in old age? Are certain features of network organisation at a young age predictive of cognitive capabilities at a later stage?3. Having quantified how brain networks change over the course of human brain development, we can begin to look for the rules governing these changes. Can we, for example, build simple models to predict the pattern of tissue loss during ageing? This question is of particular importance as more of us are living longer. This research will be conducted in the Brain Mapping Unit (BMU), at the University of Cambridge. The BMU is headed by Professor Ed Bullmore and combines researchers from a variety of backgrounds such as medicine, physics and mathematics. This unique combination offers world-class expertise in a range of areas crucial to this project and provides the perfect environment to carry out the proposed work.