Precision Medicine Doctoral Training Programme

Role of inflammation on the neurobiological features of depression & potential stratification

Project Details - Role of inflammation on the neurobiological features of depression & potential stratification

Supervisor(s): Dr Heather C Whalley, Prof Jonathan Cavanagh & Prof Andrew M McIntosh
Centre/Institute: Centre for Clinical Brain Sciences 


Major depressive disorder (MDD) is a heritable and disabling psychiatric condition which is the leading cause of disability worldwide. MDD is attributable to the effects and interactions of genetic and environmental factors, although the pathophysiological mechanisms remain poorly understood. Previous imaging studies have reported alterations in brain regions and connecting tracts within affect- and reward-processing networks, but the direction of causality remains unclear (Whalley et al., 2013; Shen et al., 2017). Increasing evidence also suggests that MDD is not a unitary disorder but is likely to represent a grouping of conditions with diverse aetiologies with the resulting heterogeneity hampering progress.

There are increasingly compelling data implicating immune/inflammatory molecules in MDD pathophysiology (Leighton et al., 2017). There are also strong indications of a key role for early life adversity and related stress-HPA axis activation (Baumeister et al., 2016). These responses may have neurotoxic effects in the brain, ultimately resulting in the clinical symptoms and progression of the disorder. However, the effects of peripheral inflammation on brain structure and function in-vivo in the disorder remain poorly understood. Further, whether affected individuals with an inflammatory aetiology, or vulnerability, constitute a definable neurobiological sub-strata of MDD also remains unknown, but has strong implications for specific and mechanistically-tailored treatment.

Research Questions:

  1. What is the relationship between increased peripheral markers of inflammation (CRP, immunoglobulins, cortisol) and brain structure and function?
  2. Can these relationships be used to sub-classify depression?


Using one of the largest single imaging studies of depression to date with neuroimaging and peripheral markers of immune-mediated inflammatory responses (STRADL n~1000, a subset of a larger cohort study Generation Scotland n=23,000 with in depth genotyping and phenotyping and access to birth cohort data), the project aims to:

  1. test the relationship between peripheral blood markers collected at the time of imaging with structural brain connectivity (DTI), cortical thickness measures (sMRI), subcortical brain volumes (hippocampus and striatum) and functional connectivity (rsFMRI),
  2. apply machine learning techniques to sub-classify a neuroimmune subtype within the diagnosis of depression.


  1. Increased inflammatory biomarkers will correlate with neuroimaging markers of decreased neuronal integrity and decreased functional connectivity between cortical and limbic areas, and abnormalities of brain structure, specifically reductions in hippocampal and striatal volume.
  2. computerized classification techniques will identify a neurobiological and aetiologically homogeneous sub-group within the depressed cases.

Training Outcomes

This proposal benefits greatly from the existing strong interdisciplinary Sackler collaboration between the University of Edinburgh and the University of Glasgow. As part of this PhD, extensive training in a wide variety of quantitative skills will be undertaken, including in MR image processing, the use of widely used neuroimaging software packages such as SPM, and FSL, as well as machine learning approaches to classification of neuroimaging data. A thorough understanding would also be gained of the complex modeling procedures offered by the powerful statistical package ‘R’, as well as familiarisation with access procedures and use of large, locally and publicly available cohorts. In addition, there is also opportunity to maximize the use of these cohorts with available genomics data and linkage to e-health records. The PhD the candidate will also gain experience in the interface between neuro- and immunobiology through supervisory links at the University of Glasgow. 

The training outcomes would therefore culminate in a sound understanding of techniques at the forefront of neuroimaging, neuroinflammation and psychiatry as applied to a large data sources in the context of transforming depression research.


  1. Whalley HC, Sprooten E, Hackett S, Hall L, Blackwood DH, Glahn D, Bastin M, Hall J, Lawrie SM, Sussmann JE, McIntosh AM. Polygenic Risk and White Matter Integrity in Individuals at High Risk of Mood Disorder. Biological Psychiatry, Vol. 74, No. 4, p. 280-286. (2013)
  2. Shen X, Reus L, Adams M, Cox S, Deary I, Liewald D, Bastin M, Smith D, Whalley H, McIntosh A. Subcortical volume and white matter integrity abnormalities in major depressive disorder: findings from UK Biobank imaging data. Scientific Reports 7, Article number: 5547 (2017).
  3. Leighton S, Nerurkar L, Krishnadas R, Johnman C, Graham G, Cavanagh J. Chemokines in health, depression and inflammatory illness: a systematic review and meta-analysis. Molecular Psychiatry 2017 in Press
  4. Baumeister D1,2, Akhtar R3, Ciufolini S4,5, Pariante CM1, Mondelli V1,5. Childhood trauma and adulthood inflammation: a meta-analysis of peripheral C-reactive protein, interleukin-6 and tumour necrosis factor-α. Molecular Psychiatry. 2016 May;21(5):642-9

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  • The deadline for 18/19 applications is 5pm on Wednesday 10th January 2018.
  • Please note all applications for the Precision Medicine DTP should be submitted to University of Edinburgh, even those applying for a project at the University of Glasgow.
  • Applicants must apply to a specific project, ensure you include details of the project you are applying to in Section 4 of your application. We encourage you to contact the primary supervisor prior to making your application.  
  • As you are applying to a specific project, you are not required to submit a Research Proposal as part of your application. 
  • Please ensure you upload as many of the requested documents as possible at the time of submitting your application.