This exciting inter-disciplinary studentship will develop and apply cutting-edge dynamical connectivity modelling to electrophysiological signals to understand the effects of major diseases in brain activity.
The successful applicant will be trained in an interdisciplinary area of research at the interface of network analysis and signal processing. The PhD will prepare the successful candidate for post-doctoral, more independent research in the applications of these techniques to neuroscience or many other scientific fields, as the applications of signal processing and network analysis are expanding nowadays.
Neurological diseases are one of the greatest threats to public health. We need new tools to tackle this looming crisis in the form of better personalized models of disease and techniques to help in their diagnosis. A very promising alternative to achieve this is the processing of electroencephalogram (EEG) signals, which record brain activity directly and non-invasively over the scalp. These multivariate time signals offer unmatched opportunities to assess brain activity.
The current state of the art highlights the importance of assessing the dependencies and synchronization between EEG signals in healthy brain function. In EEG, such synchronization is assessed through the analysis of functional connectivity: statistical dependencies between multivariate time-varying recordings of brain activity acquired at distinct locations. Network science is then used to study the connectivity patterns from a system’s perspective by decomposing complex systems into a set of elements and relationships between them. However, despite the relevance of these approaches, their actual practical use in the clinic is still limited. We need new signal processing and network analyses to better characterize the dynamical and multifaceted nature of brain activity.
Therefore, the main objective of this interdisciplinary project is to create novel signal processing methods to reveal rich temporal and topological features currently disregarded in the connectivity analyses of multivariate EEG time series.
Dr Javier Escuerdo Rodriguez
Enthusiastic and self-motivated candidates are sought with a background in electronic engineering, mathematics or related discipline. Previous experience in areas related to graph theory (e.g., networks, algebra, etc.) or signal processing (e.g., time series analysis) would be beneficial.
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.
We welcome applications from UK and EU students eligible for Research Council funding, and from students from other nationalities interested in applying to scholarships from the University of Edinburgh or elsewhere.