Large-scale neuroimaging studies of major depressive disorder, associated traits and polygenic risk

Research output: ThesisDoctoral Thesis

Abstract

Major depressive disorder (MDD) is a highly prevalent and disabling condition with a heritability of around 37%. Key symptoms of MDD include low mood and psychological distress, but the mechanisms underlying MDD and its symptoms are unclear. Genetic and neuroimaging techniques are important methods with which to better understand the aetiology and mechanisms of depression. Recently, through the availability of the UK Biobank and ENIGMA datasets, it has been possible to conduct well-powered imaging studies of heterogeneous traits like MDD, with genome-wide genetic data. These genetic data can act as causal instruments and can be utilised to identify differences in neurobiological mechanisms. The current thesis presents neurobiological associations with depressive symptoms and genetic risk for MDD using data from the UK Biobank imaging project (N range from 5,000 to 12,000). My overall aims were to investigate the neurobiological basis of MDD status, depressive symptoms and MDD polygenic risk. First, MDD case-control differences in subcortical volumes and white matter microstructure indexed by fractional anisotropy and mean diffusivity, are presented using the largest structural neuroimaging samples to date. MDD was associated with worse white matter microstructure in the thalamic-radiation subset and forceps major (posterior corpus callosum). No group difference was found for the volume of any subcortical structure. Next, associations between depressive symptom severity (including longitudinal and cross-sectional measures) with white matter microstructure were tested. Over 8,000 participants had repeated measure of depressive symptoms assessed on 2-4 occasions across 5.89 to 10.69 years. I found several novel associations between measures of depressive symptom severity (at the time of imaging, their variance within individuals over time, and with longitudinal increasing depression severity) all associated with lower white matter microstructure in the thalamic radiations. This was the first study of this size looking at imaging associations with longitudinal symptom measures and demonstrates consistent findings implicating thalamocortical connections. The third study presents results of phenotype wide association (‘PheWAS’) analysis of polygenic risk for MDD, including imaging and other available phenotypes. In total, 1,744 phenotypes were tested, covering sociodemographic, physical health, mental health, subcortical volumes, white matter microstructure assessed with FA and MD (mean diffusivity) and resting-state connectivity. I found that MDD polygenic risk was associated with MDD-related phenotypes including severity of depression and neuroticism, sleep, smoking, subjective well-being as well as neurobiological phenotypes including white matter microstructure and resting-state connectivity. In my final data chapter, neurobiological associations with cognition, as an important risk factor of major depressive disorder, were also reported. I found that higher connectivity related to the default mode network was associated with better cognitive performance. These studies suggest two features of neurobiology related to MDD traits and genetic risk. First, they implicate microstructure of thalamic white matter connections as an important biomarker for MDD risk, psychological distress and genetic risk, as reflected by its consistent associations with depressive status, depressive symptoms, within-subject variability of depression and MDD polygenic risk. Secondly, the aberrant connections within the default mode network were related to MDD phenotypes and polygenic risk. These findings, therefore, provide evidence that these features may play a key role in MDD-related neuroarchitecture.
Original languageEnglish
QualificationPh.D.
Awarding Institution
  • University of Edinburgh
Supervisors/Advisors
  • Whalley, Heather, Supervisor
  • McIntosh, Andrew, Supervisor
Thesis sponsors
Award date6 Jul 2019
Publication statusUnpublished - 2019

Keywords

  • Major Depressive Disorder
  • large sample sizes
  • longitudinal assessments
  • neuroimaging
  • thalamus
  • biomarkers for depression

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