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Priestley PhD Scholarship in Youth Mental Health: Predictors of recovery from depression in young people

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  • Full or part time
    Dr R Upthegrove
    Dr R Reniers
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Research Question:
What are the predictive biomarkers for depression recovery in adolescence?

Project Supervisors:
Dr Rachel Upthegrove, Clinical Senior Lecturer, Institute for Mental Health, School of Psychology, University of Birmingham
Dr Renate Reniers, Lecturer, Institute for Clinical Sciences, Institute for Mental Health, College of Medical and Dental Sciences, University of Birmingham
Professor Stephen Wood, Professor of Youth Mental Health, Melbourne University
Professor Nikos Koutsouleris, Professor of Psychaitry and PRONIA Project Coordinator, Ludwig-Maximilians Universitat, Munich

Depression is the leading cause of disability world-wide, with 300 million people affected (1). Adolescents and young people are particularly vulnerable to developing this illness. In the UK, the one-year prevalence of depression in adolescents is 5%. While around 60% of young people with depression will fully recover, a large proportion will have ongoing difficulties (2). Treatments such as CBT and antidepressant medication can be effective in young people with depression, yet presently it is impossible to accurately predict who will need longer-term interventions or monitoring. Identifying biomarkers which predict successful recovery can help identify those who can be safely discharged, those who need longer term support and also identify novel treatment targets.

It has been suggested that multivariate statistical methods have a great potential in identifying neuroimaging-based and other biological markers, with the combination of multiple data sources increasing specificity of prediction (3). We propose to use pattern classification techniques, i.e. machine learning, which can be designed to learn generalisable discriminative rules in high dimensional data spaces. Importantly, the discriminating power of neuroimaging-based multivariate pattern classifiers makes them particularly suitable for predictions at the individual level. Structural and functional neuroimaging features are used to determine the best classification model that reliably predicts outcome group membership.

PRONIA is an EUFP7 funded multisite study using clinical information (including childhood adversity, stress, social cognition), brain imaging and blood biomarkers (metabolomics and inflammatory markers) to identify predictive models and staging of psychoses. As part of PRONIA, there are clinical control groups without psychosis and we have collected longitudinal neuroimaging (structural and resting state MRI) clinical and blood biomarker data on 315 young people with recent onset depression.

Plan of investigation:
Multi-modal risk quantification tools will be developed to predict symptomatic recovery from depression at 6 and 12-month time points. The fusion of biomarkers with clinical predictive models provided by PRONIA work package (WP) 01 will produce prognostic indicators at the individual level that accurately identify young people most likely to recover, and those at the highest risk of enduring illness.

Training opportunities
The PhD student will develop extensive brain imaging skills, including fMRI and connectivity analysis, together with computational data analysis and advanced machine learning with NeuroMiner ( Dissemination, conference presentation and future grant writing skills will be included in the PhD training program at UoB and Melbourne. All training in imaging, machine learning and multi-modal risk quantification tools will be jointly supervised by the Universities of Birmingham (RU, RR) and Melbourne (SW). RU is an experienced clinical academic in youth mental health, RR has expertise in brain imaging analysis and adolescent brain development. SW is lead on the multimodal data fusion work package in PRONIA. One full year (year 2) of the studentship with 2 additional 12 week placements (year 1 and 3) will be in Melbourne. NK is the leader of the field of machine learning in psychiatric disorders and as project lead for PRONIA will offer advisory supervision. Within PRONIA the PhD student will have tremendous access to skilled researchers and collaborations.

The supervisory group have strong commonality and track record: with significant impact on the global research agenda and clinical services for young people with psychosis. This PhD studentship will allow full exploitation of already collected unique, unparalleled data in recent onset depression to provide ground breaking research into this common adolescent mental illness: our ultimate aim is to expand impact of our early psychosis work to the treatment of depression, a more frequent occurring and potentially equally disabling condition

Funding Notes

The IMH at the University of Birmingham was established in August 2017, with a focus on inter-disciplinary approaches to youth mental health. The IMH was developed in collaboration with colleagues in Melbourne, and we have four PhD scholarships, to be awarded 1/year for the next four years, commencing September 2018. We are awarding one scholarship this year, and advertising three projects. Each scholarship is for four years with the expectation that the student spends at least one year in Melbourne. These awards are part of the wider Priestley joint PhD programme between the Universities of Birmingham and Melbourne.


1. Depression. (2017). World Health Organization. Retrieved 2 November 2017,
2. Thapar,et al (2017). Depression in adolescence. The Lancet, 379(9820), 1056-1067.
3. Kambeitz, J., Cabral, C.,and Koutsouleris N. (2017). Detecting Neuroimaging Biomarkers for Depression: A Meta-analysis of Multivariate Pattern Recognition Studies. Biological Psychiatry, 82(5), 330-338.
4. Gregory, A., Mallikarjun, P., & Upthegrove, R. (2017). Treatment of depression in schizophrenia: systematic review and meta-analysis. The British Journal of Psychiatry, 211(4), 198-204.

How good is research at University of Birmingham in Psychology, Psychiatry and Neuroscience?

FTE Category A staff submitted: 40.80

Research output data provided by the Research Excellence Framework (REF)

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