Structural and neurochemical alterations in unipolar and bipolar major depression

Student thesis: Doctoral ThesisDoctor of Philosophy


Depressive disorders are common and debilitating conditions; however, current methods of diagnosis and treatment are suboptimal, largely due to a lack of understanding of the biological basis of these disorders. Neuroimaging has provided substantial insights in this area, but one particularly understudied area is the relationship between unipolar and bipolar depression. These disorders have similar symptom profiles but require different treatment strategies, making their diagnosis and management challenging for clinicians.

The overarching aim of this thesis is to understand differences and similarities in the structure and neurochemistry of neurobiological systems underlying unipolar and bipolar depression. This question is addressed in three ways: Firstly meta-analyses structural neuroimaging studies looking at alterations in grey and white matter were performed to identify patterns of changes that were common or specific to either disorder. Secondly, an original investigation was carried out to identify patterns of neurochemical alteration that differ between unmedicated patients with unipolar and bipolar depression. Lastly, the appropriateness of a dimensional approach to bipolarity in depression was evaluated by looking for structural neural correlates of bipolar symptoms within patients with unip9olar and bipolar depression.
The results of these studies show that although many neurobiological alterations are common to unipolar and bipolar depression, there are changes in grey matter volume that are specific to unipolar depression, and changes in white matter volume that are specific to bipolar depression. However, alterations in grey matter volume do not correlate with bipolarity when treated as a dimensional characteristic. These results contribute to our understanding of structural and neurochemical alterations in depressive disorders, and provide targets for future research into improved diagnosis of these conditions.
Date of Award2017
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorDanilo Arnone (Supervisor) & Anthony Cleare (Supervisor)

Cite this