Modelling the Development of Complex Brain Networks

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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.