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Title: The application of a diffusion tensor image segmentation technique in healthy ageing and cerebral small vessel disease : microstructural changes in the cerebrum related to cognitive decline
Author: Williams, Owen A.
ISNI:       0000 0004 6350 6666
Awarding Body: St George's, University of London
Current Institution: St George's, University of London
Date of Award: 2016
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Brain changes and cognitive decline are observed in healthy ageing and cerebral small vessel disease (SVD), an age-related disease. This thesis describes the optimisation and application of a diffusion tensor image segmentation technique (DSEG) to measure microstructural changes in healthy ageing and SVD in order to predict cognitive decline and dementia risk. Neuropsychological and magnetic resonance imaging data were collected in two prospective cohorts. 112 healthy ageing participants (aged 50-90), tested up to four years. 120 SVD patients (aged 43-89) tested annually for MR! and neuropsychological data for up to five years. Cognitive domains tested included executive functions (EF), working memory (WkM), information processing speed (IPS) and episodic memory (EM). DSEG provides a vector of 16 segments, describing brain microstructure of healthy/damaged tissue. A novel, whole-brain diffusion metric of microstructural change was calculated using the dot-product of each DSEG vector compared to a reference brain (e.g. oldest/youngest) to generate an angle-of-difference (#), describing microstructural differences of each brain compared to the reference brain. Conventional imaging was used to assess normalised brain volume, white-matter hyperintensity volume, lacunes, cerebral microbleeds, and white-matter microstructural changes. DSEG measures were associated with age-related differences in cognition (p < .05) but not longitudinal changes due to a lack of decline in cognition, in healthy ageing. In SVD, changes in individual DSEG segments and 6 were associated with decline in EF, IPS and global cognition (p < .05) over 3-5 years. DSEG accurately identified individuals who developed dementia. DSEG consistently provided more stable models for predicting cognitive decline, compared to conventional MRI parameters. This thesis shows for the first time that DSEG is a stable measure of whole-brain microstructural changes associated with cognitive differences in healthy ageing and cognitive decline in SVD. DSEG can predict who will have the most severe declines in cognition and develop dementia.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available