What goes up must come down? Using digital technology to understand the dynamic nature of mood in bipolar disorder
People with bipolar disorder (BD) experience disabling episodes of high and low mood, but how mood fluctuates between episodes could provide key insights into the condition. This project will use long-term digital mood-monitoring data from the largest sample of people with BD in the world. The student will learn cutting-edge statistical methods to test if dynamic measures of mood are associated with clinical outcomes and genetic risk factors in people with BD.
Bipolar disorder (BD) is a mood disorder where people experience disabling episodes of both high and low mood. Originally, it was thought that people with BD experience discrete mood episodes surrounded by periods of wellness, but new evidence suggests this is an oversimplification. In fact, people with BD continue to experience abnormal mood regulation even during periods when they would be considered “well”. Compared to healthy controls, people with BD experience heightened responses to emotional stimuli and more rapid changes in mood states. This project aims to answer the question: what can dynamic features of mood tell us about clinical outcomes and longterm prognosis of BD? Prior BD research suggests those with worse mood regulation have a more severe and impairing course of illness and may be more likely to have particular subtypes of BD. However, these studies often have small sample sizes because participants need to record their mood at frequent intervals over long periods of time. This prospective data collection is not traditionally done in psychiatry (which often relies on cross-sectional assessment) but may be more clinically useful.
Technological advances now make it easier than ever to collect vast amounts of data in large samples. This presents new challenges: how do we address the increased chance of missing data? How do we combine thousands of datapoints from multiple people when most statistical methods used in psychiatry allow ≤10 timepoints per person? How do we account for seasonal changes in mood? Dynamic features of mood can be defined in multiple ways (e.g. the intensity of peaks and troughs in mood over time, the amount of time taken to recover from mood disturbances), but which are most useful for aiding our understanding of BD? New statistical methods which draw on other fields such as engineering and economics can address these issues whilst also opening exciting new avenues for how we examine and conceptualise mood dynamics.
This PhD project offers a unique opportunity to be at the frontiers of mood dynamics research. The student will learn cutting-edge statistical methods for analysing data from digital technology and apply them to data from the Bipolar Disorder Research Network (BDRN) – the largest individual cohort of people with BD in the world. Over 1200 BDRN participants have completed online weekly mood questionnaires (bdrn.org/research/truecolours/) for an average of 2 years, resulting in ≥100,000 datapoints. This dataset represents a powerful resource to examine mood dynamics in real-world settings and link this with the rich clinical, demographic and biological data in BDRN.
A1) Derive traditional and novel measures of mood dynamics in people with BD. The student will draw on resources from several disciplines (psychology, engineering, economics), including cutting-edge statistical methods for analysing time series data (dynamic structural equation modelling).
A2) Test which mood dynamics are most useful for predicting course of illness and clinical characteristics of BD. The student will test associations between mood dynamics measures identified in A1 with the rich clinical and demographic data available in BDRN (e.g. age of onset, BD subtype, number of mood episodes, personality traits, presence of other psychiatric co-morbidities).
A3) Test the hypothesis that greater mood instability indicates a greater genetic susceptibility to BD. The student will test whether mood dynamics are predicted by genetic risk for BD and related disorders (e.g. major depressive disorder, schizophrenia).
There will be several opportunities for the student to take ownership and steer the project. For A1 and A2, they will identify research gaps through a review of mood dynamics literature and by collaborating with people with lived experience of BD. In A3, they will be able to decide which polygenic risk scores to include in addition to those for BD (e.g. neuroticism).
Applicants should possess a minimum of an upper second-class Honours degree, master's degree, or equivalent in a relevant subject.
Applicants whose first language is not English must meet the minimum University requirements (e.g. 6.5 IELTS)
How to Apply
A list of all the projects and how to apply is available on our website at gw4biomed.ac.uk. You may apply for up to 2 projects.
Information on how to apply to the GW4 BioMed2 MRC DTP is available here https://gw4biomed.ac.uk/doctoral-students/
Please complete the online application form by 5.00pm on Wednesday, 2nd November 2022. If you are shortlisted for interview, you will be notified by Friday 16th December 2022. Interviews will be held virtually on 25th and 26th January 2023.
For informal enquiries, please contact [Email Address Removed]
For project-related queries, please contact the respective supervisors listed on the projects.