DPhil Projects available at the BreatheOxford Lab

Dr Pattinson is pleased to accept applications from prospective DPhil applicants.  Current projects available are as follows. Please feel free to get in touch by email:  kyle.pattinson@nda.ox.ac.uk

Project 1: Interoception in health and disease - a Bayesian approach

Interoception is the term used to describe the way the brain senses the internal state of the body.  Homeostatic regulation of physiological systems such as the lungs and the cardiovascular system depends fundamentally on the correct interpretation of these interoceptive signals.

 Our research group has developed a novel method for measuring interoceptive processes via a breathing task. We wish to use and further develop this task to help us understand individual differences in interactions between the mind and body, with a particular focus on the perception of breathlessness. 

 This project will use existing and prospectively collected experimental data to test hypotheses relating to how differences in interoceptive abilities between people may relate to underlying psychology and physiology. The project will examine how interoception may change in disease states (for example in lung disease and potentially in acute mountain sickness). Analysis will include the use of computational models to help understand interoceptive decision making. A background in psychology or physiology would be useful for this project, as would an interest in learning computational approaches under supervision.

Project 2: Using big neuroimaging data to understand symptom variability in asthma.

Breathlessness in asthma is incredibly variable, both between individuals but also within individuals on different occasions. Symptoms correlate poorly with medical markers of lung damage, but more strongly with psychological state. This project will interrogate multi-modal brain imaging, behavioural and clinical data from the UK Biobank Imaging Project (https://imaging.ukbiobank.ac.uk/). 

The DPhil will give the successful candidate training in machine learning techniques, which will help harness the power of the large sample size in the UK Biobank to inform between-subject variability in asthma. The work will directly inform personalised medicine approaches for the treatment of asthma. This project would particularly suit a candidate with a strong mathematical/engineering background interested in medical applications.