Predictive modelling of the social networks influencing obesity

In 2007 24 per cent of adults and 17 per cent of children in the UK were obese; the estimated costs of treating the consequences of obesity were £1 billion in 2002 and projected to be £5.3 billion by 2025 (NHS Information Centre, Lifestyle Statistics, 2009).  Numerous social and biological factors have been implicated in the aetiology of obesity, and in 2007 the Foresight Programme of the UK Government Office for Science published a conceptual obesity system map that illustrates the multifactorial nature of this disease. With 108 variables this qualitative atlas highlights the tremendous complexity underlying obesity and the need to move away from single intervention approaches. However, this very complexity and the qualitative nature of the map have also prevented the development of a consensus on where public health interventions are most likely to make a difference.

At first glance, an unlikely correlate of this multifactorial, highly interconnected and complex network of influences is the human brain.  The complexities of the brain have been studied at many levels, from chemical reactions in individual neurons through to high-level cognitive architectures. These studies have demonstrated that small-scale behaviour, such as when and how neurons fire, can have a large-scale influence on a population. However, it is only recently that advances in modelling the dynamics of neural interactions have led to an increase in the scale of models that can be developed (Izhikevich & Edelman, 2008).  In these models, neurons are represented by biologically plausible dynamic equations, with each neuron highly connected with others. By stimulating a handful of neurons over time, behaviour emerges from the model in the form of polychronous groups: Repeating patterns of neurons that learn to activate together. These patterns result from the stimulation but demonstrate the wide influence that single inputs can establish.

These emergent patterns of behaviour may, by analogy, represent the dissemination of social influences and consequent changes in behaviour. For example, we can treat neurons as people within a population who are highly connected to others. An increase by one person in, say, watching television, modelled as a pattern of neural activity, will influence those to whom the person is connected. Over time, this influence causes the emergence of polychronous groups tuned to this pattern of activity, and hence a wider number of individuals have been influenced. The challenges are identifying social parameters that we can model and developing a plausible architecture for the influences of the parameters to be observed. With the computational efficiency of these spiking neural networks, many such parameters and architectures can be evaluated and compared with the known increase in obesity of the last 30 years in the UK to establish a benchmark. We can then test different parameter values to find target interventions.

Bringing together a team of biologists, computer scientists and mathematicians, this project aims to develop a spiking neural network model to quantify key social parameters from the Foresight qualitative map influencing the increase in overweight and obesity in the UK over the last 30 years.

E.M. Izhikevich & G.M. Edelman (2008) Large-scale model of mammalian thalamocortical systems, PNAS, 105(9):3593-3598.