This project provides the opportunity to develop and apply static and dynamic measures of functional connectivity, as indexed by resting-state functional MRI, in health and disease. Investigations championed by the successful applicant will provide new mechanistic insights regarding the central nervous system representation of pain, sensitivity and analgesia in humans. Uniquely, the project features brain and spinal cord functional MRI data, the latter historically difficult to acquire and a core strength of the supervisory team. In addition, the Neuroimaging Analysis and Statistics team will provide contemporary expertise in dynamic functional connectivity analyses. More broadly, the student will join a vibrant, diverse and engaged community at the Department of Neuroimaging.
The student will be encouraged to ‘choose their own adventure’, under the broad remit of improving understanding of spontaneous pain experiences. They will have access to a wide range of pre-existing data, acquired in experimental medicine models of acute pain, clinically relevant pain (e.g. following surgery) and also in patients with chronic, inescapable pain. Datasets examine pain across multiple body sites and the modulatory effects of differing pharmacological probes (e.g. local anaesthesia, benzodiazepines), in each case complemented by clinical, physiological, psychometric and psychophysical readouts, facilitating the development of multimodal assessment techniques. While this project focusses on pre-existing data, students wishing to gain experience in data collection will also have the opportunity to assist with other ongoing projects within the team.
In year one, the student will perform a systematic review of resting-state fMRI in pain, which will form the literature review section of their thesis and will be submitted for publication. The student will immediately commence foundation training in image analysis in the brain and spinal cord, focusing initially on validated static connectivity metrics in the brain. Similar training, albeit reduced in scope and examining pre-processed and quality assured data only, will also be available to first year rotation project students. Full time students will preregister their proposed analysis plans, in accordance with contemporary open, reproducible science practices and will submit their results for peer-reviewed publication by year end. This work will lay the ground for year two. Students will agree their analysis plans for the remaining chapters of the thesis with the supervisory team, for completion early in year three. Timely progress will facilitate dissemination of their findings with the scientific community and wider public, in written and oral form, alongside completion of the thesis in the final year.