Funded Neuroimaging PhD Studentship

 

Closing date 18th June 2020 at 5 PM. 

 

Early Life Adversity In Pain and Depression (E-PaiD): Linking structural and functional neuroimaging to healthcare data, to explore early life adversity on chronic pain, depression and analgesic use.

 

Summary

·         Clinical neuroimaging study: fMRI and brain structure analyses

·         Experience in Matlab and R an advantage

·         Training (e.g. in neuroimaging including SPM) will be provided – see below

·         Tenovus Scotland funding for all UK students

·         Start Date: 10 January 2021

 

Supervisors: Prof Lesley Colvin , Prof Douglas Steele, University of Dundee.  For details see webpages:

Work: https://discovery.dundee.ac.uk/en/persons/douglas-steele; Personal: http://dslink333.dyndns.org/ SINAPSE: http://www.sinapse.ac.uk/people/Douglas-Steele

Work: https://discovery.dundee.ac.uk/en/persons/lesley-colvin

 

Contact: dsteele@dundee.ac.uk

 

Examples of recent similar neuroimaging studies with PhD students as 1st authors

https://discovery.dundee.ac.uk/en/publications/blunted-medial-prefrontal-cortico-limbic-reward-related-effective

https://discovery.dundee.ac.uk/en/publications/abnormal-reward-valuation-and-event-related-connectivity-in-unmed

https://discovery.dundee.ac.uk/en/publications/a-causal-role-for-the-anterior-mid-cingulate-cortex-in-negative-a

https://discovery.dundee.ac.uk/en/publications/failure-of-hippocampal-deactivation-during-loss-events-in-treatme

 

Clinical Rationale
Chronic pain and depression are major clinical challenges accounting globally for four of the top seven causes of years-lived-with disability. Whilst chronic pain and depression commonly co-occur, there is limited study of common risk factors and underlying mechanisms: early life adversity (ELA) may increase the risk of both by a long term increase in the stress response, pro-inflammatory effects and brain structural and functional changes. To address this, we will use a large recently acquired dataset from the STRADL study on mood disorder which includes clinical assessments and neuroimaging data, and combine it with Generation Scotland chronic pain data and the Dundee Walker birth cohort.

Aims & Objectives

• Use linked datasets to understand the effect of ELA on central neurobiology (structural and functional effects) in patients with and without chronic pain and/ or depression.
• Explore how vulnerability to developing chronic pain and depression is affected by ELA
• Study associations between ELA, analgesic use, chronic pain and depression.
• Train student in structural and functional image analysis and health data science


Details
A unique combination of pre-existing datasets will be used to explore the links between ELA, chronic pain and depression. (Ethical approvals: 05/S1401/89 and 14/SS/0039)  Generation Scotland pain survey (Generation Scotland: Scottish Family Health Study, GS:SFHS) defined chronic pain as persisting longer than 3 months identified by a validated questionnaire which included body sites effected, pain severity and site of worst pain. The Chronic Pain Grade (CPG) questionnaire classified severity into four pain grades with pain free "controls" defined as those who reported no current pain or discomfort with GPG grade 1 excluded from analyses (1).  Stratifying Resilience and Depression Longitudinally (STRADL) was a depression-focused investigation of the GS:SFHS (4). The most recent study completed in 2019 (STRADL2019) and included 500+ Generation Scotland subjects recruited in Dundee with a particular focus on the Walker birth cohort which has meticulous records on pregnancy, labour, birth and care before discharge for births 1952-1966 (2). Data from the STRADL study includes diagnoses, detained clinical ratings and structural and functional MRI (3).


Student Training

The student will have regular scheduled supervision meeting with supervisors and will be able to access modules in the new Precision Medicine MSc in Health Data Science and the established Masters in Public Health, which brings together expertise from across the University of Dundee. The student will be based in the well-established Chronic Pain Research Group (L Colvin), with expertise in pain medicine, epidemiology and translational pain research.  The student will be trained in neuroimaging data analyses, SPM and Matlab coding, machine learning, clinical psychiatry concepts, translational neuroscience and functional neuroanatomy (by D Steele, interest in mood disorder and neuroimaging-based research, PI for Dundee STRADL). Weekly Research in Progress seminars provides an excellent training forum allowing early stage researchers to learn presenting skills in a supportive environment.

 

References
(1) Smith BH, et al. “Cohort Profile: Generation Scotland: Scottish Family Health Study (GS:SFHS). The study, its participants and their potential for genetic research on health and illness.” Int J Epidem 2013;42(3):689-700.
(2) Libby G, Smith A, et al. “The Walker Project: a longitudinal study of 48,000 children born 1952-1966 (aged 36-50 years in 2002) and their families.” Paed & Perinatal Epidemiol. 2004;18(4):302-12.
(3) Habota T, et al. “Cohort profile for the STratifying Resilience and Depression Longitudinally (STRADL) study: A depression-focused investigation of Generation Scotland, using detailed clinical, cognitive, and neuroimaging assessments.” Wellcome Open Research 2019 (on line, under review)
(4) Rupprechter S, et al “Blunted Medial Prefrontal Reward-Related Effective Connectivity and Depression" Brain 2020 (in press)

Funding Notes

Funded by a PhD Studentship award from Tenovus Scotland which includes: stipend and UK student fees, equipment, data access, GDPR storage costs, plus costs of training courses and conference presentations.  Whilst all costs with existing funding are met for UK students, international students with self-funding for additional (non-UK) costs will be considered.

 

Applicants

This study would be particularly suited to a student with a quantitative first degree (e.g. physics, mathematics, computer science, biostatistics) or relevant biomedical degree, aiming to develop a career in quantitative methods applied to medicine (e.g. medical physics). The computational work includes training in machine learning for individual patient predictions, and notably academic and commercial interest in applying machine learning to medicine is very rapidly expanding.

 

Contact: dsteele@dundee.ac.uk