Because of the ageing population, the number of people with dementia will increase dramatically in the next years. Alzheimer's disease is the most common cause of dementia and it is particularly difficult to diagnose. We need better ways to detect and monitor the changes that Alzheimer's disease causes in the brain. To achieve this, we will consider the electroencephalogram (EEG), an affordable piece of equipment that can be used outside hospitals to measure brain activity safely at several locations over the scalp (called "channels").
We will create new signal processing tools to analyse EEG brain networks based on tensor factorisations to inspect how the components of brain activity networks change with time.
We will develop a framework based on tensor factorisations (a set of algebraic and computational techniques to analyse tensors: n-mode data arrays with n>=3) to inspect the components of networks directly from the data without the need for manual intervention. We will then apply it to EEG signals. First, for each person, we will assess the coupling between channels of the EEG as a function of time and frequency. These results naturally fit into a multi-modal representation: a "connectivity tensor". Then, we will decompose the "connectivity tensor" into its underlying components. We will implement constraints to bring previous information into the decompositions, including novel ways to measure the natural organisation of the network components. Finally, we will assess the robustness of the extracted network components and we will inspect how Alzheimer's disease changes them.
We believe that revealing how the EEG network changes with time during this task could lead to a non-invasive, affordable and portable tool to monitor Alzheimer's disease. Nonetheless, this project will have much wider implications because it will benefit the signal processing, tensor factorisation and network analysis communities and the techniques will be readily applicable to other types of data, both inside and outside clinical settings.
Further Information:
- Our collaborators at the University of Copenhagen: Prof Rasmus Bro and Dr Evrim Acar
- Our collaborator at Heriot-Watt University: Dr Mario A Parra Rodríguez
- Grants on the web description
