PhD Project - ERUK-DTC: Computational models of circadian and ultradian effects in seizures rooted in physiological knowledge

Vacancy Reference Number
Closing Date
7 Jan 2022
3-year ERUK Doctoral Training Centre studentship
University of Edinburgh

Project background

Predicting epileptic seizures would be highly beneficial to patients. Data science algorithms have shown promise to predict and detect seizures but an aspect not quite fully understood yet is how the occurrence of epileptic seizures depends on the time of the day. In this PhD project, you will have the opportunity to investigate how diverse physiological characteristics interact with circadian and ultradian rhythms to promote the occurrence of seizures, and to develop signal processing and machine learning algorithms that learn the interactions of those rhythms with brain activity features for more accurate seizure prediction.

About the Project

This PhD project will provide you with an exciting opportunity to receive multi-disciplinary cross-centre training in data science and computational models, while simultaneously exploring different forms of epilepsy, the physiology of the nervous system and the effects of circadian and ultradian rhythms in seizures.

You will be trained on algorithms to explore what collection of variables in continuous brain activity predicts seizures and whether these are dependent on circadian and ultradian patterns. In parallel, you will be trained in the physiological mechanisms underpinning the origin of seizures in chronic-recording high-channel count epileptic encephalopathy rodent models. Your findings will be validated in publicly available datasets of human electroencephalogram recordings. Hence, this PhD will combine analysis of recordings from animal models, to test hypothesis and develop algorithms in more uniform data, and from humans, for a strong translational focus.

Your work will be complementary to previous efforts in the study of circadian and ultradian patterns in seizures, which has thus far not been performed in long-term recordings from well-defined animal models. The work will also delve into recent advances in machine learning algorithms. As such, the project is suitable to either: 1) candidates with background in neuroscience and interest in data science and/or machine learning; or 2) to candidates with engineering or computational background and interest in neurophysiology.