Quantifying uncertainty in perturbed brain networks: towards a decision support tool for epilepsy surgery

Lead Research Organisation: University of Exeter
Department Name: Engineering Computer Science and Maths

Abstract

Many natural and man-made systems can be described in terms of networks, in which a set of nodes is connected by edges to make a network structure. Examples include communication or transport networks, as well as networks in biological systems. Often, in real world applications, the nodes of these networks behave dynamically: their properties change over time. Understanding the ways in which network structure can lead to different dynamic behaviors is a fundamental unsolved problem in applied nonlinear dynamics. We also lack fundamental understanding of the ways in which perturbations to networks, for example the removal of particular nodes, leads to changes in network dynamics. Previous investigations into these problems have often focused on particular kinds of network behavior, such as the synchronisation of oscillations or more complex dynamics. However it is important to extend these studies to include dynamics that undergo sporadic switching between qualitatively different states. Such systems underpin the concept of "dynamic diseases" and therefore studies of perturbations to networks with these dynamics falls naturally into the EPSRC Healthcare Technologies theme.

A pertinent example is epilepsy; a prevalent neurological disorder in which nodes in networks of the brain sporadically produce abnormal activity, causing a person to suffer a seizure. We can consider that the dynamics of brain networks in the epileptic brain undergo "state-switching" from periods of healthy functioning to periods in which abnormal activity in seizures occur. There is much we do not know about why seizures occur in networks, and in particular, we often do not know how to treat a particular person's epilepsy, so that seizures no longer occur. One of the least understood forms of treatment is surgery, in which nodes of brain networks are removed, with the hope that this will stop the occurrence of seizures. In order to better understand seizures and how surgery may abate them, I will study mathematical models of brain networks that can generate seizure-like state-switching dynamics. In these models, surgery can be simulated by removing nodes from the network and quantifying to what extent state-switching is reduced.

In order to do this, I need to develop ways to choose, for a given network, which nodes should be removed in order to most effectively reduce the ability to switch from one state to another. In large networks, it soon becomes intractable to test the effect that the removal of every possible set of nodes has on its dynamics. I will therefore develop computational approaches to efficiently estimate the set of nodes that should be removed in order to limit the ability of a network to switch between states. Another critical problem is that there are many different mathematical models that can be used to generate state-switching dynamics, and these may yield different predictions for which nodes should be removed. In order to quantify this uncertainty in predictions, I will use the computational methods I develop to calculate predictions under different choices of models, and quantify to what extent predictions depend on the choice of model.

To test the applicability of the developed methods and understanding to the real world clinical problem, I will apply my methods to a set of data derived from patients who have undergone epilepsy surgery. I will derive network representations of each persons brain from this data and then use my mathematical tools to predict which nodes should have been removed in order to render them seizure free. These predictions can be tested since we know which nodes were actually removed in the patients' surgery, and whether the removal of those nodes resulted in seizure freedom.

Planned Impact

The burden of epilepsy is both deep and broad, with significant detrimental effects on patient quality of life as well as on the social networks of patients. Surgery is an option for the severest cases of epilepsy, and the methods arising from my research could both improve the success of surgery, and enable it to be offered more readily, and more widely to potential beneficiaries. My research ultimately has the potential to lead to the development of a clinical decision support tool for epilepsy surgery, which will provide a valuable additional tool that neurosurgeons, epileptologists and neurologists can use to identify the best possible surgical strategy for a given patient. I will drive this impact by working closely throughout the project with my established clinical partners, as well as hosting two workshops for patients and clinicians. The uptake of my methods by clinicians will lead to benefits for patients, with the aim to decrease morbidity and mortality due to epilepsy. The insight gained into the relationship between network structure, dynamics, and the effect of perturbations will also have further reaching clinical impact. By extending the tools I develop to include other perturbations, insight can be gained in the development of electrical stimulation devices to suppress seizures. This will improve development of alternative means by which clinicians can treat epilepsy, such as responsive implanted devices. My aim is that this impact for clinicians and patients will be realised in 5-10 years. Shorter term impact for patients and clinicians will be achieved via workshops that I will host to disseminate my ideas, engage them in the use of mathematical models to treat epilepsy, and use their feedback and ideas in my ongoing research.

Impact will also be achieved at the level of healthcare policy, since improving surgical treatment may significantly reduce the socio-economic burden of epilepsy. The cost of treating people with epilepsy within the EU is estimated to be Euro 15.5 billion per annum. My research could improve the accuracy of surgery and could also help to widen the use of surgery as an option to treat epilepsy therefore leading to a greater number of seizure free patients.

In the commercial sector, companies involved in the design of novel treatment methods for epilepsy, including responsive stimulation devices (electroceutical / optogenetic approaches) and pharmaceuticals will benefit from the research I undertake. It will provide novel information regarding the reasons why brain networks generate seizures, and which parts of the brain are best to target therapeutically. The focus on surgery in the current work can readily be extended to investigate transient perturbations, for example uncovering nodes of brain networks that could be targeted via the administration of drugs or other stimuli (e.g. electrical). Exploiting close links with clinicians will help me to keep abreast of advances and new directions in treatments for epilepsy, and will help direct this impact. I will also keep working closely with the Innovation, Impact and Business department at the University to identify potential commercial opportunities and facilitate impact in the next 5-10 years.

The PDRA hired on the project will receive training in multi-disciplinary science: an area that is lacking people with appropriate training, skills and experience. The PDRA will benefit from regular phone or skype meetings with clinicians, as well as at least one visit to a clinical epilepsy department, where they will be able to experience the pre-surgical evaluation pathway first hand. Furthermore, they will be embedded in a research environment that emphasises public and patient involvement. The development of the PDRAs career will further benefit labs in which they become involved in the future. This impact will therefore take place over the course of the project and into the longer-term.

Publications

10 25 50