Machine learning based investigation of the imaging and genetic profile of drug-resistant epilepsy
A 4-year PhD studentship is available in the UCL Centre for Medical Image Computing The funding covers an annual tax free stipend (£16,777) and tuition fees. As the studentship is partially funded by the EPSRC the standard EPSRC eligibility criteria apply, please see EPSRC website for further details. The successful candidate will join the UCL CDT in Medical Imaging cohort and benefit from the activities and events organised by the centre.
Epilepsy is a group of neurological conditions that share the common characteristic of epileptic seizures. There are many types of epilepsy and many types of seizure. The diagnosis of epilepsy typically follows the occurrence of two or more seizures. While anyone can develop epilepsy at any point in life, it is most commonly diagnosed in children and people over 65 years of age. Currently, about 60 million people are suffering from epilepsy worldwide and over 500,000 in the UK alone.
Anti-epileptic drugs (AED) are intended to reduce the frequency of seizures or even completely eliminate them. If AED treatment works, it allows people living with epilepsy to lead normal lives. However, in about one third of people none of the available drugs or combinations of drugs stop the seizures. These cases are considered treatment-resistant epilepsies and the underlying biology is still poorly understood.
In this project, the successful candidate will help to advance knowledge about drug-resistant epilepsy. Using a large database of neuroimaging data from people with epilepsy, the first aim is to establish the imaging signature of drug-resistant epilepsy compared to drug-responsive epilepsy. That is, which brain regions show different cortical thickness or structural and functional connectivity changes in resistant vs responsive epilepsy? The second phase of the project involves an imaging-genetics approach to elucidate the genetic origin of drug-resistant epilepsy, which may help to identify drug targets for currently drug-resistant epilepsy.
Applicants are expected to have a first degree in Computer Science or Computational Biology or Biomedical Engineering based subject passed at 2:1 level (UK system or equivalent) or above. Good working knowledge of C++ and/or Python and/or R is highly desirable. Some experience with medical image analysis or genomics data is also desirable.
To make an application for this project please send a CV and cover letter detailing why you want to apply for this studentship and why you believe you are suitable for the studentship, to Dr Andre Altmann at firstname.lastname@example.org.