Huntington's disease (HD) is a devastating inherited neurodegenerative disorder with an onset commonly in mid-adulthood. To date there are no disease-modifying treatments that have been shown to slow the progression or delay the onset of HD in humans; however, several compounds have shown promise in animal models of HD. To assess their efficacy in humans, robust and sensitive markers of disease progression are required. Biomarkers capable of detecting premanifest changes are critical for clinical trials of treatments to delay disease onset. Current clinical measures are limited by floor and ceiling effects and lack sensitivity to change with time especially in premanifest subjects. This thesis investigates the utility of volumetric magnetic resonance imaging (MRI) for tracking structural changes in premanifest and early-manifest HD. Several approaches are described including hypothesis-driven region-of-interest (ROI) investigations focused on the caudate nucleus, cingulate cortex and lateral ventricles, as well as hypothesis-free whole-brain analyses at the voxel-level. Where cross-sectional work has shown promise on the basis of group separation, investigations are extended to longitudinal analyses in order to track within-subject progression. This work included developing and validating a novel automated technique for quantifying change in caudate volume from registered serial MRI. A longitudinal voxel-based morphometry technique was adapted and applied to assess the regional progression of atrophy in HD over 27 months. Suitability of these measures for use as biomarkers of progression in large clinical trials is discussed in terms of sample-size requirements, automation of measurement and reliability.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:625359 |
| Date | January 2010 |
| Creators | Hobbs, N. Z. |
| 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/20460/ |
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