Schizophrenia is a devastating mental illness and a huge disease burden in terms of cost. The individual is typically affected in early adulthood, thus loosing the best years of their life. The stigma of mental illness and pattern of downward social drift also results in families and society being adversely affected. With advances in neuroscience and neuroimaging, psychiatrists can advance their understanding of schizophrenia as a disease of the brain using biological models. This thesis investigates how volumetric magnetic resonance imaging (MRI), structural MRI techniques such as magnetisation transfer imaging (MTI), diffusion tensor imaging (DTI), and novel techniques such as optical coherence tomography (OCT) and visual function testing may be used to elucidate the neuropathology of schizophrenia in-vivo, in addition to explaining the cognitive deficits that are commonly observed. . The following studies are included in this thesis: 1) A diffusion tensor imaging (DTI) study to explore white matter abnormalities in first episode psychosis and correlations with cognitive performance. 2) An exploratory study utilizing OCT to investigate whether retinal nerve fibre layer thickness varies between patients with schizophrenia and healthy controls. 3) A longitudinal study using MRI and MTI to examine structural brain changes following first episode psychosis and correlating these findings with cognitive performance. 4) An investigation of chromatic vision in schizophrenia spectrum disorders and correlations between hue discrimination ability and cognitive performance. 5) A cross-sectional comparison study of grey matter volume and associations with oculomotor function in first episode patients and healthy controls.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:602916 |
| Date | January 2014 |
| Creators | Chu, E. M. |
| Publisher | University College London (University of London) |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://discovery.ucl.ac.uk/1421353/ |
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