Modelling the Development of Complex Brain Networks

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.

Graph theoretical methods are increasingly being used to study biological and medical problems at a systems level. In the brain, understanding the network structure of neurons at the microscopic level, and of brain regions at the macroscopic level is crucial to elucidating the mechanisms underpinning both healthy and impaired brain-function.

While we now possess a sophisticated set of measures to describe the topology of static brain networks, the driving forces that shape the networks into these characteristic topologies remain largely unknown. More generally, the temporal evolution of networks has so far received very little attention both theoretically and in its applications to neuroscience. My proposed research aims to fill these gaps by studying the temporal evolution of brain networks in two developmental epochs of particular clinical interest: (i) the maturation of brain networks during adolescence and (ii) the changes in network structure associated with ageing.

My key hypothesis is that many complex network-level changes in human brain organization can be parsimoniously explained (and therefore modelled) in terms of simple trade-offs between cognitive function, spatial constraints and energetic costs. To test these ideas, I will first characterize developmental changes in brain networks using both novel and existing graph theoretical methods. Second, I will correlate the changes observed in various network features with changing abilities in several cognitive domains. This will allow me to consider the functional role of network properties that may offset energetic cost in a trade-off model of human brain development. Based on these insights, I will then build computational models that predict the pattern of network growth and degeneration in terms of biologically plausible rules and simple physical constraints.

My proposed research has three facets, which will each impact on a different set of beneficiaries both within and outside academia.

1. Advances in Neuroscience
Perhaps the most direct impact of my work will be a better understanding of brain maturation and senescence. Beyond the advancement of scientific knowledge, this may result in downstream medical and pharmaceutical applications with potentially important impacts on health and wellbeing in society, as well as on the UK's economic competitiveness.

2. Methodological Advances in Network Science
Methodological advances in network analysis will immediately impact on a large number of academic disciplines from epidemiology to economics. As described in Pathways to Impact, I will work to ensure that these advances are propagated further to the companies, policy makers and other organizations that use network analysis in their products and/or decision-making.

3. Communication and Dissemination
As described in the Communication Plan, I am committed to disseminating my research, its motivation and its outcomes to a broad audience, from the general public to Parliament. Through the various activities described in Pathways to Impact I hope to:
(i) Raise awareness of the relevance of science to both everyday life and policy making.
(ii) Work towards a more balanced and better-informed public perception of mental health and healthy ageing.